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Social marketing of water and sanitation products: A systematic review of peer-reviewed literature

· W.D. Evans a , , ,

· S.K. Pattanayak b , ,

· S. Young a , ,

· J. Buszin c , ,

· S. Rai b , ,

· Jasmine Wallace Bihm a ,

doi:10.1016/j.socscimed.2014.03.011

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Highlights

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Water and sanitation behaviors can be marketed like commercial products.

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There has been no systematic review of theory, research, and practice in this area.

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We identified 32 articles over a 22 year period that met search criteria.

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Evaluations show improvements in mediators but mixed results in behavior change.

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Social marketing can improve water and sanitation programs worldwide.

Abstract

Like commercial marketing, social marketing uses the 4 “Ps” and seeks exchange of value between the marketer and consumer. Behaviors such as handwashing, and products such as those for oral rehydration treatment (ORT), can be marketed like commercial products in developing countries. Although social marketing in these areas is growing, there has been no systematic review of the current state of practice, research and evaluation.

We searched the literature for published peer-reviewed studies available through major online publication databases. We identified manuscripts in the health, social science, and business literature on social marketing that used at least one of the 4 Ps of marketing and had a behavioral objective targeting the behaviors or products related to improving water and sanitation. We developed formalized decision rules and applied them in identifying articles for review. We initially identified 117 articles and reviewed a final set of 32 that met our criteria.

Social marketing is a widespread strategy. Marketing efforts have created high levels of awareness of health threats and solutions, including behavior change and socially marketed products. There is widespread use of the 4 Ps of marketing, with price interventions being the least common. Evaluations show consistent improvements in behavioral mediators but mixed results in behavior change.

Interventions have successfully used social marketing following widely recommended strategies. Future evaluations need to focus on mediators that explain successful behavior change in order to identify best practices and improve future programs. More rigorous evaluations including quasi-experimental designs and randomized trials are needed. More consistent reporting of evaluation results that permits meta-analysis of effects is needed.

Keywords

· Social marketing;

· Health promotion;

· Behavior change;

· Water;

· Sanitation;

· Diarrheal disease

1. Introduction

At its core, marketing is about an exchange of value between the marketer and consumer. If the marketer can promote a product or service to make the consumer perceive sufficient value, the consumer is more likely to purchase it. The marketer's objective is to create value for consumers and thereby financial benefit for the marketer or her clients. Social marketers use the same powerful idea in a different way – not to sell products and services for the benefit of the marketer but to promote socially beneficial causes and behaviors for the benefit of the audience ( Hastings, 2007 ). A growing body of evidence shows that marketing is highly effective in this arena as well ( Evans et al., 2008 ).

Like commercial marketing, social marketing uses the basic principles of the “marketing mix”, or 4 “Ps” of place, price, product, and promotion ( Borden, 1964 and Kotler and Lee, 2008 ). Just as a brand of toothpaste can be effectively marketed by making it available to consumers in convenient locations, pricing it right relative to competitors, endowing it with attractive perceived benefits, and advertising it in persuasive ways with sufficient frequency, behaviors such as handwashing and treatment products such as those for oral rehydration salts (ORS) can also be marketed. In the case of handwashing, we may need to think differently about factors such as ‘price’ (time and effort, or convenience) and ‘product’ (the social or functional benefits gained from the behavior) but the basic approach is the same ( Evans and Hastings, 2008 ).

Recently, there has been growth in social marketing to promote healthy drinking water and sanitation practices, as well as treatments to prevent water-related illnesses such as diarrheal disease ( Devine, 2008 and Freeman et al., 2009 ). However, to date there have been no known efforts to systematically review and assess the extent, nature, and evidence of effectiveness for social marketing in global water and sanitation programs. The purpose of this paper is to systematically review the published literature on this topic and evaluate the state of practice and evidence in the field. Specifically, we sought to identify 1) the presence or absence of specific social marketing activities in each intervention, 2) the presence or absence of each of the 4 Ps of marketing in the interventions, and 3) the outcome of the evaluation conducted in these social marketing interventions. Based on results of this review, we discuss the need for interventions that incorporate more Ps, more outcomes research, and more research on the effectiveness of specific intervention strategies and combinations of the 4 Ps.

2. Methods

We conducted a systematic search of the published, peer-reviewed literature using all relevant major online research literature databases (specified below) and following widely accepted methods for systematic review ( Higgins and Green, 2011 ). We note that social marketing and interventions focused on water and sanitation are also widely represented in unpublished reports and other ‘gray’ literature. However, we focused on peer-reviewed literature in order to ensure quality of evidence and consistency with accepted systematic review practices.

We identified as relevant any manuscripts in the published health, social science, and business literature that used at least one of the 4 Ps of marketing, had a behavioral objective targeting water and sanitation related behaviors, and had a health objective targeting diarrheal disease. We based the review methodology in part on methodologies from a previous review of branded social marketing campaigns conducted by the lead author ( Evans et al., 2008 ). Specifically, we searched the following health, social science, and business databases:

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PubMed, PsycINFO, Web of Science (includes Science Citation Index Expanded, Social Sciences Citation Index, and Arts & Humanities Citation Index), Communication & Mass Media Complete, Academic Search Premier, Business Source Premier, CINAHL, Health Source: Nursing/Academic Edition, and Health Source: Consumer Edition.

Search terms included the following: Diarrhea/Diarrhoea, Hygiene, Water, Sanitation, Social Marketing, Health Promotion, Health Communication, Community Intervention, Community Health, Behavior Change. We selected these terms based on the authors' experiences in the field and conducting previous reviews, and in consultation with a medical research librarian.

For completeness, we also searched literature known to the authors, including publications on water and sanitation intervention studies in the developing world. In particular, the bibliographies of two recent meta-analyses on social marketing and mass media intervention were reviewed and potential citations screened following the methods described ( Snyder et al., 2004 and Grilli et al., 2002 ).

We searched all sources listed above in the date range of 1991–June 2013). Based on this process, we created a database of all 352 unduplicated articles on social marketing interventions regarding diarrhea and water or sanitation. We immediately excluded 235 articles regarding ‘breastfeeding and diarrhea’, ‘zinc supplementation and diarrhea’, and articles that did not include an intervention of any kind. After this initial phase, we identified 117 articles.

Next, we reviewed the articles based on our specific criteria for inclusion in study. Namely, we screened them for reports on interventions that: 1) were original research (not review papers, meta-analyses, or commentaries); 2) utilized some form of social marketing principles (i.e., reported on use of 1 or more of the four Ps); 3) targeted behavior relating to water or sanitation; and 4) targeted diarrhea prevalence and/or morbidity as a health objective. We also screened to ensure the articles included specific reports of evaluation or implementation of social marketing activities, defined as coordinated efforts to promote, distribute, or use pricing strategies to encourage adoption of various products aimed primarily at diarrheal disease prevention. Based on this in-depth screening process, we excluded 85 articles and identified 32 articles for inclusion in the study. Fig. 1 summarizes the review process.

RISMA diagram of systematic review process.

Fig. 1.

PRISMA diagram of systematic review process.

Figure options

Note that due to the diverse nature of the literature on social marketing interventions in this area, and the varying methods of reporting outcomes, we chose not to attempt a meta-analysis of effects of reviewed interventions on behavior. Rather the purpose of this paper is to describe the nature of the interventions and literature, hopefully promoting more uniform reporting of outcomes in future.

Once the review sample of articles was identified, the first two authors individually read each of the articles in-depth and coded them for specific content reported in the results section. The results of all reviews were compiled and discussed by the reviewers. Potential sources of differences in assumptions and approach in coding articles were identified and discussed. A consensus was reached about coding, and common procedures adopted where discrepancies had been identified.

3. Results

Table 1 provides a summary of basic information gleaned from each water and sanitation article reviewed. The articles dealt with interventions relating to household water treatment, handwashing, safe disposal of feces and personal hygiene. The studies covered 14 developing countries, mostly in South Asia and Africa.

Table 1.

Overview of studies in this review.

Topic area	Citation	Public health campaign	Research design	Location
Safe water system	Dunston et al. (2001)	Safe Water System (Sûr’Eau)	Paired study; randomized	Madagascar
Safe water system	Makutsa et al. (2001)	Water, Sanitation, and Education for Health (WASEH) Project	Non-experimental	Kenya
Safe water system	Quick (2003) .	Safe Water System (SWS)	Experimental; field trials	Zambia, Madagascar, Kenya
Point-of-use water treatment	Parker et al. (2006)	Safe Water System (SWS)	Non-experimental; Paired Study	Kenya
Handwashing; safe disposal of feces	Curtis et al. (2001)	Saniya	Observational study; randomized	Burkina Faso
Handwashing; dishwashing	Pinfold (1999)	Social marketing – (posters and curriculum within schools, leaflets, stickers, handwashing containers, posters	Experimental; microbiological indicators (fingertip contamination	Thailand
Faucet-fitted earthen vessels; point-of-use water treatment	Dongre et al. (2008)	Community-Led Initiatives for Child Survival (CLICS) program	Quasi-experimental	India
Safe Water System	Garrett et al. (2008)	Water, Sanitation, and Education for Health (WASEH) Project	Quasi-experimental	Kenya
Handwashing; point-of-use water treatment; use of local vessels	Luby et al., 2004a and Luby et al., 2004b	Home-based intervention	Quasi-experimental	Pakistan
Handwashing; point-of-use water treatment	Luby et al. (2006)	Children's books, notebooks, pens and pencils	Cluster randomized control	Pakistan
Handwashing	Luby et al., 2004a and Luby et al., 2004b	The Karachi Soap Health Study	Cluster randomized controlled trial	Pakistan
Handwashing	Luby et al. (2005)	The Karachi Soap Health Study	Cluster randomized controlled trial	Pakistan
Hygiene practices; infrastructure	Nanan et al. (2003)	Water and Sanitation Extension Programme (WASEP) project	Case Control	Pakistan
Hygiene practices	Onyango-Oumaa et al. (2005)	Participatory health education with students; peer training among students	Quasi-experimental	Kenya
Handwashing; dishwashing	Pinfold and Horan (1996)	Social marketing techniques to promote dishwashing and hand washing	Quasi-experimental	Thailand
Point-of-use water treatment; safe storage	Quick et al. (1999)	Point-of-use water disinfection; safe storage; community education; weekly visits by community health workers	Quasi-experimental	Bolivia
Safe water system	Migele et al. (2007)	Water treatment with bleach, safe storage, and behavior-change communications	Observational	Kenya
Safe water system	Stockman et al. (2007)	Safe Water System, “Waterguard”	Observational	Malawi
Safe Water System	Thevos et al. (2003)	Safe Water System, “Clorin”	Experimental	Zambia
Safe Water System	Thevos (2000)	Safe Water System, “Clorin”	Experimental	Zambia
Safe Water System	Thevos (2000)	Safe Water System, “Clorin”	Experimental	Zambia
Feces disposal; appropriate places for defacating	Yeager et al. (2002)	Promotion through nurses at health clinics – use of IPC, video presentations and leaflets	Quasi-experimental	Peru
Hygiene; sanitation	Kariuki et al. (2012)	Capacity building and empowerment	Repeat cross-sectional	Kenya
Hand washing; water treatment; latrines	Greene et al. (2012)	School-based water, sanitation, and hygiene (WASH) intervention	Cluster randomized trial	Kenya
Hand hygiene; drinking water treatment and storage	Ryman et al. (2012)	“Waterguard”	Experimental	Kenya
Zinc-promotion	Wang et al. (2011)	Social Marketing Plus for Diarrhoeal Disease Control: Point of Use Water Disinfection and Zinc Treatment (POUZN) project	Experimental	Nepal
Handwashing	McDonald et al. (2011)	“No Germs on Me”	Observational	Australia
Water treatment	Wood et al. (2012)	“Waterguard”	Observational	Malawi
Water treatment	Luby et al. (2008)	Marketing of flocculant-disinfectant	Experimental	Guatemala
Water treatment and storage	Lule et al. (2005)	Safe water system	Experimental	Uganda
Water treatment and storage	Quick et al. (2002)	Safe water system	Experimental	Zambia
Safe water system	John et al. (2007)	Safe water system	Retrospective ecological study	Kenya

Table options

The interventions used a wide range of health communication and social marketing strategies, including mass media, interpersonal communication (IPC), community outreach and visits to households by health workers. New and social media, while rapidly growing forms of communication, were not identified as strategies in any of the reviewed papers. High levels of awareness of the promoted health messages were reported.

Table 2 provides a summary of the topic areas that were the focus of social marketing interventions. Most interventions focused on behaviors practiced inside the home, such as handwashing, point-of-use water treatment, dishwashing and various hygiene practices. Several interventions incorporated more than one desired behavior, such as appropriate water storage in combination with point-of-use water treatment.

Table 2.

Topic area (n = 32). a

Variables	Frequency	%
Safe water system	10	31.3
Point-of-use water treatment	12	37.5
Handwashing	9	28.1
Dishwashing	2	6.3
Improved water supply	1	3.1
Use of local vessels	2	6.3
Infrastructure	1	3.1
Appropriate places for defecating/Safe disposal of feces	3	9.4
Ground sanitation	1	3.1
Hygiene practices	3	9.5
Latrines	2	6.3
Safe storage	4	12.5

Several articles addressed multiple topics.

Table options

Table 3 provides a summary of the social marketing development described in the water and sanitation articles reviewed. In some cases, studies described specific scientific theories used in the development of the social marketing effort used. Psychology and communication theories were the most commonly used theories, found in 26% and 23% of the studies reviewed, respectively. Some of the articles described intervention efforts based in formative research such as interviews and focus groups. Examination of the studies in terms of the use of the 4 Ps of marketing revealed a majority using each of the four marketing techniques. Almost all articles (96.8%) used “product” and “promotion” marketing techniques. The product P was represented by 1) safe water treatment products and 2) behavioral product approaches in which hand-washing and sanitation behaviors were marketed as having social and functional benefits. The price P was only explicitly addressed in the safe water treatment product interventions, with subsidies to reduce costs of the marketed systems used to encourage purchase and use. The place P was represented by 1) community-based sales approaches, 2) home visit approaches, and 3) health clinics to simplify distribution. The promotion P was represented by mass advertising, community outreach, use of branded gear such as t-shirts, and promotion by health care providers at clinics. Community outreach was a heavily utilized marketing channel present in 96.8% of the intervention efforts.

Table 3.

Social marketing development (n = 32).

Variables	Frequency	%
Scientific theory
Communication	7	21.9
Marketing	2	6.3
Psychology	8	25.0
Other	1	3.1
Formative research
Focus groups	10	31.3
Interviews	9	28.1
Other	2	6.3
Marketing P's
Product	31	96.9
Price	20	62.5
Place	26	81.3
Promotion	31	96.9
Marketing channels
Paid mass media	13	40.6
Unpaid mass media	4	12.5
Earned media	0	0.0
Community outreach	31	96.9
Community mobilization	19	59.4
Mobile phones	0	0.0
Social media (not Mobile)	0	0.0
Other marketing techniques
Audience segmentation	16	50.0
Message tailoring	3	9.4
Other	0	0.0

Table options

Table 4 provides a summary of the study design and outcomes in the water and sanitation articles reviewed. Most of the articles reviewed described studies with an observational design; the remaining studies were equally split between experimental and quasi-experimental designs. Almost all articles reported on the study sample size and sample characteristics. Multivariate analysis was used to report statistics in over half of the studies. Most of the studies (75%) aimed to assess behavioral objectives and clearly stated these objectives. Almost all of these studies reported product/behavior awareness or reactions to the interventions.

Table 4.

Study design and outcomes (n = 32).

Variables	Frequency	%
Research design
Experiment	9	28.1
Quasi-experiment	9	28.1
Observational study	14	43.8
Sample size reported	30	93.8
Sample characteristics described	26	80.6
Response = follow-up rate reported	12	38.7
Statistics reported
Descriptive	10	31.3
Multivariate analysis (ANOVA = regression)	18	56.3
Path analysis	1	3.1
None Reported	2	6.3
Objectives clearly stated
Product/behavior awareness or reactions	18	56.3
Pre-behavioral outcomes	17	53.1
Behavioral outcomes	24	75.0
Effects reported
Product/behavior awareness or reactions	28	87.5
Pre-behavioral outcomes	12	37.5
Behavioral outcomes	27	84.4
Estimate of precision reported	24	75.0

Table options

4. Discussion

Table 1 , Table 2 , Table 3 and Table 4 outline various aspects of the studies that used social marketing in water and sanitation interventions. We analyzed activities that each of the interventions used and examined the extent to which they incorporated the four Ps of social marketing.

4.1. Product

Over a third of the studies reviewed used the Safe Water System, which promotes point-of-use water treatment (specifically a sodium hypochlorite solution), safe storage and behavior change. The sodium hypochlorite solution is marketed under different names - Sur’ Eau in Madagascar, Klorin in Kenya, WaterGuard in Malawi – but is always locally produced and sold. The use of a distinctive brand name and logo echoes the marketing techniques used by the private sector to ensure product differentiation. However not every intervention employed such a strategy.

In Dongre et al. (2008) , for example, unbranded faucet-fitted earthen vessels were sold to consumers in India; in Nanan et al. (2003) and Pattanayak et al. (2009) , the programs provided unmarked latrines to rural households in Pakistan and India. Branding can also extend to the entire campaign. In Ahmed et al. (1993) , hygiene behaviors in Bangladesh were marketed under a “Clean Life campaign”.

The product, in both the Safe Water System and other interventions, is in some cases behavioral or attitudinal. Every reviewed study also had as a product the health gains that arose from a hygiene-related behavior like handwashing, water treatment or safe disposal of feces.

4.2. Place

Place in social marketing refers to the distribution channels and outlets through which tangible products are made available to consumers. In the case of health-related behaviors, place could also mean the location where the consumer performs the desired behavior, or accesses information about the product. Where the interventions surveyed involved the transactional exchange of a tangible product, the programs tried to facilitate availability and easy access by utilizing local resources.

In Madagascar, for example, community-based sales agents were trained to provide the sodium hypocholorite solution ( Dunston et al., 2001 ). In Karachi, Pakistan, fieldworkers visited households to distribute soaps for handwashing ( Luby et al., 2005 ), while lidded storage vessels were distributed along with water treatment solutions to households in Nicaragua ( Macy and Quick, 1998 ).

Programs also used home visits to promote health related behaviors. Volunteers in Burkina Faso trained volunteers to conduct monthly house-to-house visits to educate households about the benefits of handwashing and safe disposal of feces ( Curtis et al., 2001 ). Where the intervention involved performances or the use of print or visual media, public spaces in the community were also utilized. In northeastern Thailand, for instance songs about hygiene messages were broadcast through village loudspeakers ( Pinfold and Horan, 1996 ). Researchers used weekly prayer meetings at local mosques in rural Bangladesh to talk about cleanliness as a common goal ( Ahmed et al., 1993 ). Targeted interventions also focused on specific groups. In a study in western Kenya, for example, health education was given to schoolchildren in grades three and five using drawings, role-play, songs and poems ( Onyango-Ouma et al., 2005 ).

4.3. Price

Price in social marketing could refer to the provision of monetary subsidies (about whose effectiveness there is considerable disagreement) or the costs of time and effort that individuals and households have to bear in adopting the intervention. For the purposes of this paper, however, we focus on monetary subsidies/incentives.

In interventions that used the Safe Water System, household water treatment solutions were sold at subsidized rates, typically a small fraction of the market price. In Madagascar, for instance, sodium hypocholorite solution was sold at 30 cents for a 500 ml bottle ( Dunston et al., 2001 ), while in western Kenya, the price was 33 cents a bottle ( Makutsa et al., 2001 and Garrett et al., 2008 ). In Wardha, India, a 50 ml bottle was sold for 30 cents, along with an earthen vessel fitted with a plastic faucet ( Dongre et al., 2008 ). Similarly, in Zambia, water vessels were supplied at subsidized rates ( Thevos et al., 2003 ).

Water treatment solutions were also sold at reduced prices to wholesalers and retailers to stimulate sales ( Makutsa et al., 2001 ). In addition, health workers also sometimes received small commissions as incentives ( Garrett et al., 2008 ). Where the intervention took the form of investments in sanitation infrastructure, households were often asked to bear part of the costs of construction. For instance, CARE Kenya paid for 40% of the costs for providing new latrines in rural communities, but households were expected to pay the remaining 60%, and also provide labor for the latrine construction. Similarly, in a community-led sanitation intervention in Orissa, India, households below the poverty line had to pay 15% of the cost of a new latrine, while other households paid the entire cost ( Pattanayak et al., 2009 ). Another solution, called WaterGuard, was initially distributed at low cost and later provided freely at antenatal women in order to overcome cost barriers ( Wood et al., 2011 ).

4.4. Promotion

Promotion is perhaps the most visible aspect of social marketing. It aims to generate demand for the product by imparting information and changing attitudes. Most of the water and sanitation intervention surveyed used extensive promotion techniques. One common strategy – used in interventions in Burkina Faso, Pakistan, Uganda and Bolivia – was to train local health workers/volunteers to impart hygiene education through household visits. Another strategy was to organize neighborhood meetings to promote awareness of hygiene behaviors. For example, in Wardha, India, community-based village co-ordination committees were formed to act as go-betweens between households and program workers ( Dongre et al., 2008 ).

Some studies focused on particular spaces of interaction, like clinics and schools. For instance, in Homa Bay, Kenya, nurses at a maternal and child health clinic were trained to communicate information about water chlorination to their clients ( Parker et al., 2006 ). Schoolchildren too can sometimes act as health behavior change agents. In Bondo, western Kenya, schoolchildren underwent participatory health communication training, where they learnt about hygienic behaviors through songs, poems and drawings ( Onyango-Ouma et al., 2005 ).

Print and electronic media were used in several studies to disseminate hygiene-related information. Advertisements on radio, television and in newspapers, pamphlets and signs on buses, walls and billboards were just some of the techniques used. Some interventions used community-related activities to get the message of hygiene out. In Nyanza, Kenya, program workers organized soccer tournaments and Klorin quizzes to spread awareness about their product ( Makutsa et al., 2001 ). In Thailand, comic books and T-shirts were developed to support program activities ( Pinfold, 1999 ).

4.5. Design and impacts of interventions that employ social marketing: water treatment at the household level

In the 1990s, The Centers for Disease Control and Prevention (CDC) and the Pan American Health Association (PAHO) designed the “Safe Water System,” an intervention using point-of-use treatment, proper storage, and behavior change to improve water safety in developing countries. The behavior change component of the intervention employs media campaigns, community mobilization, and interpersonal communication techniques to increase the connection made between water quality and disease ( Centers for Disease Control and Prevention, 2013 ).

Organizations such as CARE International and Population Services International (PSI) have used the Safe Water System (SWS) framework to promote household-level water treatment in nineteen countries across five continents. Participating organizations have often grounded the intervention, tailoring the social marketing component to fit the particular needs of each target community. For example, in Madagascar, CARE and PSI used branding and media campaigns in conjunction with community mobilization in a randomized study to promote the use of the point-of-use treatment system and increased sales from 76,000 bottles in March 2000 to 250,000 bottles in December of the same year ( Dunston et al., 2001 ). CARE also implemented the SWS intervention in Western Kenya and used social marketing and motivational interviewing combined with subsidized prices to promote home water treatment and safe storage. A non-experimental study found that the project resulted in adoption rates of 33.5% for chemical water treatment and 18.5% for clay pots modified for safe water storage ( Makutsa et al., 2001 ). When CARE used the SWS intervention in southern Kenya in another non-experimental study, they grounded the intervention to the target community by incorporating social marketing with a nurse training program focused on the six steps of proper handwashing. One year later, 71% of clients of the clinic had chlorine residuals in their drinking water and 34% were observed using proper hand washing techniques ( Parker et al., 2006 ).

Motivational interviewing has been found to be an effective technique for promoting SWS adoption and promotion of behavior changes, as described below, in several African countries ( Thevos et al., 2000 ). A randomized trial in Zambia, Madagascar, and Kenya demonstrated the effectiveness of household SWS promotion interventions ( Quick, 2003 ).

4.6. Handwashing

Diarrheal diseases and acute respiratory infections are two of the leading causes of mortality among children under five worldwide. Handwashing promotion is the most cost-effective public health intervention available and has the potential to prevent 50 percent of both diarrheal diseases and ARIs ( Luby et al., 2005 and Cogswell and Jensen, 2008 ).

Programme Saniya, launched as part of a randomized observational study in Burkina Faso, aimed to reduce the prevalence of diarrheal disease by promoting correct handwashing techniques and proper stool disposal. For three years, researchers implemented a combination of mass media campaigns, street theatre, house-to-house visits, and a hygiene-focused school curriculum. Post-intervention surveys and observations revealed a significant increase in the occurrence of handwashing with soap ( Curtis et al., 2001 ).

Another example of the effectiveness of social marketing in handwashing promotion interventions is a study conducted in a rural area of northeast Thailand, in which researchers used various social marketing techniques (songs, print media, theatre) along with workshops for health workers and students to promote dishwashing and handwashing. Researchers also eliminated literacy barriers by including illustrations in all print media. The results of the randomized controlled study showed a 39 percent decrease in the incidence of diarrheal disease among children under 5 years. When subjects were asked to detail messages learned from various sources, they reported retaining the most information from print media, namely stickers and leaflets ( Ahmed et al., 1993 ).

In several squatter settlements located in Karachi, Pakistan, researchers conducted a randomized control trial on the effect of community health promoters on the incidence of diarrhea, impetigo, and pneumonia. One treatment group received antibacterial soap and the other received plain soap. For one year, community outreach workers made weekly visits to households in both treatment groups to promote proper hand washing techniques. There was no significant difference between the two treatment groups, but treatment households had 53 percent lower incidence of diarrhea, 36 percent lower incidence of impetigo, and 50 percent lower incidence of pneumonia than households in the control group ( Luby et al., 2005 ).

4.7. Oral rehydration therapy

Oral rehydration therapy (ORT) is the most cost-effective method for lowering childhood mortality during a severe episode of diarrhea ( UNICEF, 2008 ). Social marketing saves lives by enabling organizations to reach and educate large segments of the population simultaneously. In India, researchers compared mothers who had seen television spots about ORT to those exposed to ORT through other means. They found that the mothers who learned about ORT through television spots were significantly more likely to correctly apply the knowledge in daily life.

Social marketing can also enhance the effectiveness of a private sector approach to increase the use of ORT. In Kenya, public clinics provided oral rehydration packets free-of-charge, while private pharmacies sold flavored packets. This approach was coupled with a mass media campaign educating the public on the availability and proper use of ORT, which resulted in a significant increase in ORT sales through both clinics and pharmacies and an increase in the percentage of residents correctly utilizing the packets ( Kenya et al., 1990 ).

4.8. General hygiene education

The United States Agency for International Development, Environmental Health Project, UNICEF, and Water Supply & Sanitation Collaborative Council designed the Hygiene Improvement Framework, outlining the necessary components of a successful diarrhea prevention program ( UNICEF, 2010 ). One of the three pillars of a diarrhea prevention program is hygiene promotion, which includes social mobilization, marketing, community participation, and raising awareness, all of which can be achieved through the “four P's” of an effective social marketing campaign ( Kleinau et al., 2004 ).

Organizations can use social marketing in a variety of ways to promote proper hygiene and lower the prevalence of diarrhea. In rural Bangladesh, village leaders implemented the “Clean Life” campaign, a community-based hygiene intervention focusing on ground sanitation, personal hygiene, and food hygiene. The quasi-experimental study found improved growth and a reduction in the number of undernourished infants ( Ahmed et al., 1993 ).

5. Conclusion

The goal of a published body of literature is to advance the field by providing insight and direction to future research. As such, we identified a number of characteristics that are important to the field of social marketing relating to water and sanitation behaviors and products, and should be included in future published studies of such public health efforts. The current literature represents a broad span of quality, with most studies providing clear reports of the strategy and actions taken to develop and evaluate the marketing effort, but others providing only minimal information in a few of the identified domains.

We addressed the specific aims of this systematic review set forth at the outset. First, we identified the types of activities that water and sanitation social marketing interventions efforts have used. There have been widespread efforts to change sanitation practices ranging from personal hygiene to building sanitation infrastructure in villages. Programs have used both individual behavior change and infrastructure improvement schemes to create an environment in which behavior change can occur.

Second, we described the marketing mix used in social marketing in this area. Overall, there is widespread use of each of the 4 Ps of marketing, with some interventions not having an explicitly reported ‘price’ strategy. This is a strength of the programs reviewed, and it is noteworthy that many use an explicit branding strategy to build long-term relationship with product consumers, such as in ORT and SWS.

Third, we identified the research evaluation methods, design, and reported impacts from the social marketing efforts. Evaluations were generally well designed under challenging local circumstances and there is consistent evidence of improvements in behavioral mediators (ie, knowledge, attitudes, beliefs targeted by the social marketing). Virtually all interventions reported on such mediators. Examples included intentions to use water and sanitation products, self-efficacy to use them following interventions such as motivational interviewing and training, and social norms that use of such products was widespread and a preferred practice in the community post-intervention. Evidence for behavior change is mixed, with successes clustered in SWS programs.

This review has some limitations. First, this study shared the well known limitations in reviews of social marketing, which stem mainly from variable use of keywords and descriptors for interventions ( McDermott et al., 2005 ). Not all relevant published studies may have described themselves using social marketing terms, and thus may have been missed by this review. However, we used a variety of search terms and employed expert inputs and knowledge of the field to address this concern. Second, we identified relatively few sanitation related social marketing interventions. This may be related to the first limitation, and the possibility that social marketing aimed at sanitation appears mainly in the gray literature. However, there is a need to promote these studies and publication of sanitation social marketing interventions.

Future research should focus on the mechanisms of behavior change. While many programs were successful, it is unclear from this review what behavioral mediators were responsible. For example, many programs attempted to change social norms. Are strategies aimed at changing personal hygiene practices by promoting the norm that such behaviors are widespread among peers and socially approved most effective? Future studies need to focus squarely on these questions in order to identify best practices in water and sanitation social marketing and diffuse the most effective behavior change strategies.

Financial competing interests

The authors certify that they have received no financial support of any kind related to preparation of this manuscript. The authors certify that they hold no stocks or shares in any such organization. The authors certify that they have not applied for any patents or received any reimbursement from organizations holding patents related to this manuscript. The authors certify that they have no other competing financial interests.

Non-financial competing interests

The authors certify that they have no competing non-financial interests related to this manuscript.

Author's contributions

WDE co-conceived the study, directed the literature search, reviewed articles, and wrote sections of the manuscript. SKP co-conceived the study, reviewed articles, and edited sections of the manuscript. SY searched the literature, created the tables, edited sections of the manuscript and compiled references. JB assisted in literature searching and edited sections of the manuscript. SR assisted in literature searching and edited sections of the manuscript. JWB assisted with literature searching and edited sections of the manuscript. All authors read and approved the final manuscript.

Acknowledgment

The authors gratefully acknowledge their institutions, The George Washington University and Duke University, for their support of this publication effort. While no financial support was provided and the institutions played no role in the manuscript preparation, both institutions encouraged the authors to pursue this project and allowed the use of institutional facilities to support the work.

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Corresponding author.

Open by default: a proposed copyright license and waiver agreement for open access research and data in peer-reviewed journals

Iain Hrynaszkiewicz* and Matthew J Cockerill

· * Corresponding author: Iain Hrynaszkiewicz [email protected]

Author Affiliations

BioMed Central Ltd, 236 Gray’s Inn Road, London, WC1X 8HB, UK

For all author emails, please log on.

BMC Research Notes 2012, 5:494 doi:10.1186/1756-0500-5-494

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1756-0500/5/494

Received:	11 June 2012
Accepted:	4 September 2012
Published:	7 September 2012

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Copyright and licensing of scientific data, internationally, are complex and present legal barriers to data sharing, integration and reuse, and therefore restrict the most efficient transfer and discovery of scientific knowledge. Much data are included within scientific journal articles, their published tables, additional files (supplementary material) and reference lists. However, these data are usually published under licenses which are not appropriate for data. Creative Commons CC0 is an appropriate and increasingly accepted method for dedicating data to the public domain, to enable data reuse with the minimum of restrictions. BioMed Central is committed to working towards implementation of open data-compliant licensing in its publications. Here we detail a protocol for implementing a combined Creative Commons Attribution license (for copyrightable material) and Creative Commons CC0 waiver (for data) agreement for content published in peer-reviewed open access journals. We explain the differences between legal requirements for attribution in copyright, and cultural requirements in scholarship for giving individuals credit for their work through citation. We argue that publishing data in scientific journals under CC0 will have numerous benefits for individuals and society, and yet will have minimal implications for authors and minimal impact on current publishing and research workflows. We provide practical examples and definitions of data types, such as XML and tabular data, and specific secondary use cases for published data, including text mining, reproducible research, and open bibliography. We believe this proposed change to the current copyright and licensing structure in science publishing will help clarify what users – people and machines – of the published literature can do, legally, with journal articles and make research using the published literature more efficient. We further believe this model could be adopted across multiple publishers, and invite comment on this article from all stakeholders in scientific research.

Introduction

Much has been written about, and support stated for, sharing and publishing scientific data, in recognition of the benefits for the economy [1], scientific discovery [2] and public health [3]. Maximizing the potential of scientific data sharing for the discovery of new knowledge involves reducing barriers to data dissemination, reuse, reproducibility and integration. Licensing, ownership, copyright and intellectual property present legal obstacles to data integration and reuse, which has led to the development of, and calls for, licensing standards for open data; where data are explicitly placed in the public domain with legal rights of the owners waived [4].

BioMed Central has previously stated that the concept of open data, analogous to its policy on open access to journals, goes beyond making data freely accessible. Data should also be free to distribute, copy, re-format, and integrate into new research, without legal impediments [5]. This position is consistent with the Panton Principles, which hold that for society to reap the full benefits of scientific research the published body of knowledge must be open – readily available such that it can be evaluated, reused, criticized and integrated with other knowledge without restrictions [6]. For the remainder of this article the term ‘open data’ is reserved exclusively for data available according to these principles.

Unfortunately much data – and other content – freely available on the web are available under restrictive or ambiguous terms, which risks impeding or potentially criminalizing secondary users of scientific data. According to evidence submitted to the UK Government’s intellectual property review by the Wellcome Trust, 87 per cent of the material housed in the full-text scholarly archive UK PubMed Central is unavailable for legal text and data mining [7]. A key finding of a more recent report, commissioned by JISC, was a need to overcome legal restrictions and uncertainties surrounding text mining of scientific literature [8].

Indeed, as recognition of the value of shared life science data has increased, so has recognition of intellectual property and copyright as barriers to progress. Writing in Nature in 2009, Schofield et al., urged that “any restrictions on use should be strongly resisted and we endorse explicit encouragement of open sharing” [9]; and Conway and VanLare in JAMA, in 2010, called for US health care data to be available without intellectual property constraints [10]. Waiver of all intellectual property rights in research data is central to the achievement of an “information commons”, advocated by organisations such as Sage Bionetworks, to enhance the (slowing) pace of drug discovery.

The genomics community has shown leadership in establishing a framework for an “information commons”, engrained in the Bermuda Principles, and have established built-in temporal latencies to data for knowledge (when data are released), and rights (when rights restricting use are removed) [11]. Researchers in this community typically must release their genetic sequence data immediately, and within 6–12 months release their exclusive rights in that data. During this relatively short embargo researchers have their opportunity to exploit the data for their discoveries, after which the community at large can benefit, if they wish, from the new data. A similar model for data release has since been proposed for clinical trials, although is probably far from implementation [12]. A number of factors seem to have led to a successful culture of sharing in the genomics community: a need to collaborate and share to achieve a major goal (the sequencing of the human genome); effective mechanisms and infrastructure for sharing large amounts of data (well-funded genetic sequence databases); scientific community and funding agency mandates to share data; and importantly, in the context of this article, successful collaborations with the publishing community. Journals, their editors and publishers, supported implementation of the Bermuda Principles by, for example, requiring accession number for data deposits as a condition of manuscript submission or publication.

BioMed Central in its August 2010 open data statement [5] and subsequent cross-publisher Publishing Open Data Working Group meeting identified that open data in journal publications could be implemented by specifying that, from a specific date, any author submitting to a journal or publisher agrees to dedicate the data elements of their article and supplementary material (in particular, additional data files; also known as “supplementary” data files) to the public domain [13]. Much of the contents of academic journals could be considered as data but licensing terms cannot be applied retroactively by publishers without authors’ consent, and any changes to authors’ agreements should ideally be made in consultation between authors and publishers.

This article aims to describe practically what is needed from publishers to explicitly dedicate data within open access journals to the public domain, and discusses the implications of this development for authors, editors, publishers and funders of research. Illustrative examples and use cases are provided throughout the article. In this article “open access” is defined according to the Budapest Open Access Initiative definition [14].

Applying the right license to published research and data

The internet has revolutionized the way we access and distribute information, enabling virtually anyone to post content online. There is much potential in rapidly sharing content on the web, but releasing content without information, or with ambiguous information, about if and how it can be shared and reused can also cause problems – especially for data.

Open access publishing of peer-reviewed journal articles commonly utilizes the legal tools – licenses – prepared by Creative Commons. BioMed Central, Public Library of Science, Nature Publishing Group, BMJ and many others publish open access articles where the authors retain the copyright to their work. Authors typically apply a Creative Commons attribution license (CC-BY), or variation of it, which means anyone is free to copy, reuse, distribute and make derivatives from their article provided that there is attribution of the original author(s). However, many “open access” publishers place restrictions on commercial reuse of published articles (papers) and on creation of derivative works, which can include text mining in some jurisdictions. Additionally, some commercial publishers’ terms and conditions, by contract, can prevent text mining in any jurisdiction. Commercial use restrictions have been strongly discouraged – their use described as amounting to “pseudo open access” – as authors will not reap the full benefits of paying for open access publication (for example figures could not be uploaded to Wikipedia with commercial use restrictions) [15,16]. BioMed Central supports unrestricted use of open access content including commercial use and as such requires authors to apply a CC-BY license by default. BioMed Central’s full text corpus of open access research articles published under CC-BY is available for free distribution, reuse and creation of derivatives with no commercial use restrictions – with data mining research strongly encouraged [17]. For data published by scholarly publishers, the Association of Learned and Professional Society Publishers and International Association of Scientific, Technical, & Medical Publishers (STM) issued a joint statement in 2006 supporting sharing of raw datasets among scholars and recommending that publishers do not require transfer of copyright in data submitted for publication [18].

Copyright and data

The policies and guidelines of many academic institutions advise researchers to establish intellectual property and copyrights at the start of any project (although whether the issue of data ownership is consistently addressed by researchers is unclear [19]). Copyright cannot generally be asserted in facts, only the ways in which they are presented. At a basic level raw data are merely simple, mathematical, descriptions of facts and to claim copyright a scientist would need to exert individual judgment, expression or skill in their representation. For example, Einstein could not claim copyright in the formula E = mc2, but could in text explaining the theory behind it [20]. You could conclude from this that copyright and associated licenses and attribution requirements cannot legally be applied to data. However, there are many levels at which data – particularly digital data derived and integrated from different sources – and collections of data and metadata can operate and be represented, and many ways in which copyright law is applied in different jurisdictions.

In the US the law focuses on creativity (“Copyright does not protect facts, ideas, systems, or methods of operation, although it may protect the way these things are expressed”) but in Australia originality is more important – and copyright may well apply to research data “in the same way that it applies to written works like books, journal articles and reports” [21]. In the European Union “sui generis” rights exist to protect data within digital databases – effectively, copyright – which can, furthermore, be implemented differently by member states. Because of these substantial international legal differences regarding how copyright can be applied to data, there are inherent difficulties in ascertaining the extent of copyright in a dataset. A more comprehensive summary of the different approaches to copyright in data and databases can be found in [22]. All of these issues compound the uncertainty about what an individual or machine (such as a computer crawling the web) can do, legally, with information they download from the internet, including from journals.

Licenses and waivers for data

A license is a legal instrument for a copyright holder or content producer to enable a second party to use their content, and apply certain conditions and restrictions to those uses. A waiver is also a legal instrument but is designed for a rights holder to give up their rights, rather than assert them. For a comprehensive guide to the different approaches to the licensing of research data see [23].

Placing restrictions on the reuse of scientific information, particularly data, slows down the pace of research. Furthermore, legal requirements for attribution ingrained in licenses such as CC-BY can prohibit future research across large collections of content – as commonly happens in data mining research. Consider the Human Genome Project: a watershed moment for scientific data sharing and collaboration. Without the collective effort of many different research institutions, commercial organizations and individual scientists the sequencing of the human genome would not have been possible. But if a researcher wishing to query the human genome database as part of a new research project was legally required to attribute all the – probably thousands – of data contributors, by providing a link back to or citation, this would be unmanageable, and probably un-publishable in the context of a traditional research paper’s reference list.

International legal differences, described earlier, are another important reason to apply specific, appropriate legal tools to data. Also, it can be unclear what license to attach to copyright in a dataset or structure (for example a textual description of building the dataset could fall under CC-BY, but if source code were used rather than text it might not). This is an area of confusion where no licensing standard exists. Therefore, to eliminate legal impediments to integration and re-use of data, such as this stacking of attribution requirements in large collections of data, and to help enable long-term interoperability an appropriate license or waiver specific to data should be applied. There are a number of conformant licenses and waivers for open data [24], of which Creative Commons CC0 (http://creativecommons.org/publicdomain/zero/1.0/ webcite) is widely recognized. Under CC0, authors waive all of their rights to the work worldwide under copyright law and all related or neighboring legal rights they have in the work, to the extent allowable by law. Legal experts have recommended the use of standard, globally accepted licenses for data instead of developing ad hoc models [25].

The case for CC0 for scientific data

The Creative Commons’ website catalogues a number of different organizations – publicly and privately funded – which use CC0 for data [26]. These include:

• Genomes Unzipped, which “aims to inform the public about genetics via the independent analysis of open genetic data, volunteered by a core group of genetics researchers and specialists”

• GlaxoSmithKline (GSK), a leading pharmaceutical company, has dedicated data on more than 13,500 compounds known to be active against malaria to the public domain [27].

• The British Library and Cologne-based Libraries, which have released large amounts of bibliographic data under CC0 [28]

• FigShare (http://figshare.com/ webcite), a freely-accessible repository for scientific content including images, video and data, uses CC0 for datasets

Data repositories are particularly relevant users of waivers and licenses for research data. Although there are many data repositories in life sciences (for a list see http://www.datacite.org/repolist webcite), which are growing in size and number, not all scientific domains have a common repository and journals often function as repositories when data are included as additional files (supplementary material). Dryad (http://datadryad.org/ webcite) is an international repository for the datasets supporting published, peer-reviewed journal articles across the biosciences which requires authors to explicitly place deposited data in the public domain using the CC0 waiver. An entry on the Dryad weblog sets out cogently why CC0 is the most effective solution for achieving its goals:

“By removing unenforceable legal barriers, CC0 facilitates the discovery, re-use, and citation of [that] data…

“Furthermore, Dryad’s use of CC0 to make the terms of reuse explicit has some important advantages:

• interoperability: Since CC0 is both human and machine-readable, other people and indexing services will automatically be able to determine the terms of use.

• universality: CC0 is a single mechanism that is both global and universal, covering all data and all countries. It is also widely recognized.

• simplicity: there is no need for humans to make, and respond to, individual data requests, and no need for click-through agreements. This allows more scientists to spend their time doing science.” [29]

Dryad’s policy ultimately follows the Science Commons’ recommendations, set out in their Protocol for Implementing Open Access Data [30].

The online laboratory notebook software LabArchives (http://www.labarchives.com/ webcite), which includes the ability to share data privately and to publish datasets publicly and permanently online, also uses CC0 for public datasets [31].

Concerns about public domain dedication of data

Credit where credit’s due – attribution and citation

A common concern about moving from an attribution license, such as CC-BY, to CC0 for data and waiving attribution rights is that academic credit (citations) will be lost if there is no longer a legal requirement to attribute the original rights holder (author). While attribution can sometimes be achieved in the same way as citation the two practices serve different purposes. Attribution is a legal tool designed to permit copying, distribution, and creation of derivative works such as translations. As copyright does not protect ideas (in the US), to give scientists credit for their ideas the established norm in scholarly communication is citation [32].

Consider a scientist paraphrasing a concept put forward in a peer’s research article. He or she does not legally have to cite their peer’s published paper, but it is beneficial or possibly essential for the validity and reliability of the subsequent work to specify the source(s) of assertions made. Community norms are enforced by the community, and in science unacceptable citation practices are typically identified and resolved through peer review, and the publication ethics and editorial policies of peer-reviewed journals. See Table 1 for common citation and attribution events in scholarly communication. These examples are for illustrative purposes and do not constitute legal advice.

Table 1. Attribution vs. citation in (re)uses of open access scientific content published under a Creative Commons Attribution license (CC-BY)

The examples in Table 1 demonstrate that, although they can sometimes be achieved in the same way, attribution and citation are not the same. Citations are much more important and relevant than attribution when tracking scholarly outputs and giving appropriate credit for individuals’ contributions.

Compared to legal requirements, cultural norms benefit from flexibility, and can evolve with the community which established them. In other words, using norms retains control and decision making within the research community, instead of taking it out of our control and handing it to lawyers and judges. Many scientific ideas, after a number of years, become undisputed and the community may deem it unnecessary for credit to be rigorously applied. For example, it would today be very unusual for an article describing a DNA sequencing experiment to cite the original work by Watson and Crick that elicited DNA’s structural properties. This is a cultural norm at work, where an idea is now so widely accepted, and the initial authors clearly recognized for their discoveries (in citations and prizes) a citation is not needed.

Jonathan Rees, formerly of the Creative Commons, said of community norms for influencing behavior over legal requirements: “For widest latitude of use and best scalability, and therefore greatest return to the research community, the entirety of the data set, including any incidental copyrightable elements, should be dedicated to the public domain. Note that public domain is not incompatible with a request for attribution or other terms of use following community norms. Such a request may be as effective – or more effective – at getting users to follow desired practices as any attempted legal restrictions [33].”

To our knowledge there have been no empirical studies of the citation of scholarly datasets assigned public domain dedication licenses compared to a comparable group available under attribution licenses. However, given public domain dedication – and specifically CC0 – is intended to maximize the potential for data discovery, reuse and therefore citation, it would be reasonable to hypothesize that citation potential of public domain data would be unaffected or might even increase. With CC0 there is no need for transfer agreements and preconditions, which inherently impede further (re)uses of data. Sharing of microarray experimental research data underlying journal articles has been associated with increased citation share [34], and increased reproducibility and repeatability of results [35]. In social science data collections funded by the National Institutes of Health and National Science Foundation in the US, data sharing has been associated with “many more times the publications” than collections where data were not shared [36]. Linking of publications to supporting datasets has also been associated with more citations to the linked paper in the marine science journal Paleoceanography, according to a conference abstract, and in the field of astronomy according to a pre-print paper [37].

Competition

Researchers who apply CC0 to their data, or any other product of their scholarship, waive all rights in that data allowable by law. Such a waiver has been described as an “unattractive option for data whose creators have yet to fully exploit them, academically or commercially” [23]. This is true, but a waiver or license required by a journal or publisher generally applies only in the context of data submitted for publication. If a portion of a large database was analyzed and an additional data file included for publication the larger, unpublished, body of work would retain whichever license the researchers, their employers or institutions require. In other words, researchers remain in control of what they chose to publish – what they submit to a journal – and a change in the publishers’ license does not affect this. Moreover, waivers and licenses for journal articles do not replace existing, established community norms for sharing of some data types (e.g. depositing microarray and genetic sequence data in appropriate databases) – nor do they affect requirements of many journals for sharing readily reproducible materials including raw data on request [38].

There is a trade-off between the additional opportunities which may result from transparency (such as new collaborations, secondary use) and the threat, improbable or otherwise, that opening up data may be valuable to competitors. Certain types of research, such as genetic sequencing to elucidate susceptibility to disease, generates far more data than one research team could conceivably analyze – which logically lead to sharing and collaboration. A number of companies have opened up some of their data and seen benefits [39]. A lot of data may have commercial value but much raw data, such as protein sequences of potential drug targets, are just the beginning of a knowledge-discovery process. More can be gained by “pre-competitive” sharing with the waiver of intellectual property. Such an approach is being championed by Sage Bionetworks in the US [40].

Plagiarism

Plagiarism is research misconduct and an unfortunate but ineliminable occurrence in scholarship. Plagiarism and the potential for plagiarism have increased with the proliferation of digital access to information [41]. Plagiarism is often not illegal, but it is certainly unethical, and undoubtedly damaging for the career of someone guilty of perpetrating plagiarism. Effective online tools for detecting plagiarism exist, such as CrossCheck (http://www.crossref.org/crosscheck/index.html webcite), as does human detection via the peer-review process. Removal of a legal requirement for attribution for data elements of articles would be unlikely to impact on the potential for plagiarism. In addition, CC0 would not apply to the main, copyrightable text of articles.

Other safeguards

Public domain dedication of data does not mean that those who generated the data cannot express certain wishes about how the data are used. The Panton Principles frequently asked questions (FAQs) state: “You should always aim to follow any reasonable requests made by the data owners/publishers. These may be explicit or may be implicitly understood by the community. You should make an effort to understand any relevant ‘community norms’ for the data you are using [42].”

A code of conduct has been proposed for those wishing to reuse clinical trial data obtained from other researchers [43] and a clinical trial dataset published in the journal Trials, by Sandercock et al., has requested that “any publications arising from the use of this dataset acknowledges the source of the dataset, its funding and the collaborative group that collected the data.” [44].

Electronic publishing platforms provide further safeguards to ownership and authorship of published content, in the form of date stamping manuscript submissions and version control in some repositories, such as Edinburgh DataShare (http://datashare.is.ed.ac.uk/ webcite).

Citation of articles and datasets is facilitated through standard citation formats – such as those advocated by DataCite where persistent dataset identifiers, such as digital object identifier (DOI) names, are displayed as linkable, permanent URLs – and are increasingly supported by some publishers [37].

What do we mean by data?

There are numerous definitions of data. According to Wikipedia data “are qualitative or quantitative attributes of a variable or set of variables. Data are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables,” [45]. Data can exist electronically or non-electronically, so a definition that includes electronic access is important, in the context of integration, reuse and data mining of online scholarly content. The Copyright, Designs and Patents Act, part of UK statute, uses a broad definition of databases incorporating electronic access:

“Databases(1)In this Part “database” means a collection of independent works, data or other materials which—

(a)are arranged in a systematic or methodical way, and

(b)are individually accessible by electronic or other means.

(2)For the purposes of this Part a literary work consisting of a database is original if, and only if, by reason of the selection or arrangement of the contents of the database the database constitutes the author’s own intellectual creation.”

Other definitions, such as at the United States National Science Foundation ‘DataNet’ program, have been broader and implied anything capable of existing digitally, including publications and software, could be considered data [46]. The former definition is more broadly applicable to data which can be harvested, mined or downloaded from open access journals – and to which CC0 rather than CC-BY should apply. This inevitably means information which can be processed by machines as well as being transferred electronically by them (e.g. papers attached to emails) [47]. It is not possible to comprehensively define and account for all data and data file types, particularly given the rapidly evolving nature of data and text mining applications, but a number of general examples and definitions follow below. We strongly encourage readers to comment on these data definitions and provide additions and amendments. These examples intentionally do not include domain-specific data standards (agreed upon formats for disseminating and presenting particular types of scientific experiments), which are comprehensively catalogued by BioSharing (http://biosharing.org/?q=standards webcite).

Tabular data

Data elements organized in columns and rows – a table – are extremely common in scientific publications. While attribution would be required for reproduction in whole or in part of a table as presented in a journal publication, the individual values and collection of values should be considered as data and therefore open. Data, in the course of a scientific experiment, are usually collected at a greater level of detail than are reported in a paper, with tables reporting summary or mean values. Although these data are aggregated from the raw data they remain numerical representations of a fact, and therefore data. Tables are furthermore often included as additional files in a variety of formats including PDF, HTML/XML, DOC and Excel/CSV. Ideally all tables included in the main body of a journal article should also be included as additional CSV files – an open, machine-readable format – but when they are not present as tables in journal articles CSV would represent good practice for tabular data. Proprietary file types, such as Microsoft Office, and formats which are not readily editable, such as PDF, are not recommended for tabular data provided as additional data files.

Graphs and graphical points

Graphs, graphical representations of relationships between variables, are ostensibly images and therefore not, when considered as a collective entity, data. However, the individual data points underlying a graph, similar to tables, certainly are. An example of best practice when submitting a manuscript with a graph to a peer-reviewed journal would be for authors to also submit accompanying CSV tables with the corresponding data points, so that graphs could be re-plotted. Although this practice is required by some specific journals it is not widespread. However, software tools exist that are capable of “scraping” underlying data points from graphs and images (for example http://www.chardta.com/ webcite) and can be useful, for example, for enhancing the discoverability of scientific information by exposing underlying data points to internet search engines.

XML

According to the World Wide Web Consortium (W3C), “Extensible Markup Language, abbreviated XML, describes a class of data objects called XML documents and partially describes the behavior of computer programs which process them” [48]. XML is widely used as a standard for data transfer and for creating versions of works intended for machine reading, and therefore to be used as data. Therefore for our purposes we can assume XML files are data. XML has many applications in science and is frequently published with journal articles as additional files in BioMed Central journals as well as underlying the online articles themselves. XML forms the basis of many domain-specific data standards such as Gating-ML in flow cytometry, FuGE-ML in functional genomics, GelML in gel electrophoresis and so on (see: http://biosharing.org/standards_view webcite).

Bibliographic data

Bibliographic data have been historically described as information not included in the full text and images included with an article, which includes reference lists. “Core bibliographic data” have been further described as “data which is necessary to identify and / or discover a publication” and defined under the Open Bibliography Principles:

• names and identifiers of author(s) and editor(s)

• titles

• publisher information

• publication date and place

• identification of parent work (e.g. a journal)

• page information

• Uniform resource identifiers (URIs) [49]

Therefore, these core bibliographic data should be considered open data.

RDF

Resource Description Framework (RDF) is a standard language for encoding data and metadata on the web. It is designed to indicate the relationship between online objects in a human and machine-readable way, and facilitate merging of data between different sources even if the underlying schemas of the sources are different. RDF forms the basis of the semantic web, and is a core component of achieving Tim Berners-Lee’s vision of Linked Data on the web [50]. RDF provides new opportunities for data and knowledge management in life sciences, chemistry, and publishing. All BioMed Central journal articles, for example, contain embedded RDF, which conveys harvestable information about content, such as authors, licensing information and the unique identifier for the article [51].

What aren’t data?

Although source code may be represented as data and is certainly machine readable there are a wide range of existing licensing systems and community norms that exist around software. Therefore we choose to regard software, compiled code, and source code as a separate category and not as data. Specific licenses and repositories have been developed for source code for software and Open Source Initiative compliant licenses [52] are recommended. Files pertaining to programming languages can be included as additional files with journal publications, either directly in formats such as SQL or indirectly in compressed or packaged file formats such as ZIP.

There are myriad file types which can be published as additional files but amongst the most common, in BioMed Central journals, are those usually pertaining to text and written works – PDF and DOC/DOCX and HTML (a full list of published additional data files is available on request from BioMed Central [53]). Caution is recommended in the interpretation of these objects as open data.

Implementing a variable license for open access research and data

Setting date (CC) Zero

Creative Commons licenses (CC-BY, specifically) have provided an effective and penetrative solution for digital copyright in open access scientific works (papers). But as the nature of the published scientific paper (article) has evolved then so too should the copyright and licensing structure which authors apply to their content. Published articles are increasingly collections of different digital objects, perhaps including a few thousand words of text, half a dozen images and a similar number of CSV or XML (data) files.

The fact that CC-BY is a suboptimal license for data does not mean that the many thousands of published authors have done something wrong, as CC-BY was (and often still is) the best instrument available when copyright license agreements for open access journals were prepared (although, CC-BY version 4.0, currently in draft form, aims to tackle the issue of sui generis database rights [54], described earlier). A number of data repositories, including Dryad and FigShare, initially asked authors to make their deposited data available under a Creative Commons attribution license but have since changed their policy (https://twitter.com/#!/figshare/statuses/50241486796754944 webcite). However, licenses and waivers cannot be applied retroactively by a publisher without explicit consent of the copyright holder(s) – in the vast majority of cases at BioMed Central, the authors. A small number of datasets remain in Dryad which are not available under CC0, as explicit agreement from data depositors (rights holders) could not be obtained to change the terms of data release [29].

A change to BioMed Central’s standard license agreement to include a CC0 waiver for published data would remove ambiguities about the copyright and attribution requirement status of parts of published articles and associated data files, and enable instead the application of scientific, cultural norms that meet the needs of scientists better than an inflexible legal instrument [33,55]. To implement open data in journal publications the new license agreement would need apply to all authors from a specific date, such that any author submitting to a journal/publisher agrees to dedicate the data elements of their article and additional files to the public domain. A proposal for how this could be reflected in published articles’ copyright license statement follows:

"© 2012 <Author> et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 webcite), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data included in this article, its reference list(s) and its additional files, are distributed under the terms of the Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ webcite; http://www.biomedcentral.com/about/access webcite).”

This would be further indicated in article metadata, RDF and on the journal and publisher’s policy pages and author submission pages online. This addition to the statement aims to succinctly summarize that data in published articles generally originates from three sources – tabular data or text-minable factual data (e.g. numerical instances of a particular word or phrase, such as gene/protein names) in the main body of an article; additional (supplementary) files such as XML and CSV file extensions that include data; and the reference list (bibliographic data). From a legal perspective, we might need to be more comprehensive about our definitions of data (see ‘What do we mean by data?’, above) in the full legal code of a new license and in guidelines accompanying this proposed change. And practically, authors would need to agree to apply two different legal tools to content they submit for publication as part of the submission process – with links to both CC-BY and CC0, for example, in journal submission checklists.

The need for change in author behavior, as a result of this proposed modification, is minimal. CC0 would not apply to nor affect the availability of data not submitted for publication; we would instead be asking authors to apply different terms of use to a proportion of the content they already publish. By submitting a manuscript and all associated files to a BioMed Central journal authors already confirm that they agree to all terms of the BioMed Central Copyright and License Agreement [56], including that they are able to apply a CC-BY license. This proposed change to the license agreement should provide clarity to the licensing of specific components of published articles and does not represent a substantial change to the overall license agreement for authors’ published work.

Open data by default – opt out

Public domain dedication of data, while universally desirable, is not always universally possible. Authors, for the most part, prepare research articles in the context of employment by a third party and can be subject to licensing terms which supersede the standard terms of a publisher. This already happens for a small proportion of the content published by BioMed Central and undoubtedly by other publishers, such as some articles funded by the World Health Organization and US government. Therefore, any submitting author who is not able to agree to all terms of the BioMed Central Copyright and License Agreement should contact the journal editorial office at the earliest opportunity – ideally before, during or immediately after manuscript submission. The onus is on the authors and, if applicable, their employers to decide on the applicability of the publisher’s standard license agreement to their work and whether an exception is needed. Alternatives to the standard license agreement can be discussed. This process of checking suitability of the standard license agreement, and requesting alternatives where necessary, therefore already happens for all manuscripts submissions. The owners of the rights (if any) in the data must decide if their data can be made open. According to the Panton Principles FAQs “[i]n most cases, the people who make a decision to publish, and were intimately involved in the generation of the data, should be making this decision.” [42]

Publishing platform developments

Implementing a new license agreement will have technical as well as policy and procedural implications. While it is not possible to specify these changes in detail here, and is beyond the scope of this document, the following would be essential for implementation:

• Tagging of articles and data files published under a non-standard license agreement (where authors have opted out of the new default open access-open data license)

• Editing standard embedded license information in article XML metadata and RDF and a tool to automate insertion of non-standard licensing terms

• Insertion of license information to additional files and associated metadata

Furthermore, the following would be desirable to enhance the discoverability and usefulness of open data in journal articles:

• Tagging and classification of published data files, for example by file type

• A tool to automatically discover and aggregate additional files

• A tool to (retrospectively) associate data objects with papers on the web

• Approaches to associating published datasets with journal articles which go beyond hyper-linking, such as through linked data methods

• Searching within and filtering of additional files

Open data in science use cases in published and unpublished contexts

There are many uses for open data but probably many more as yet unknown. As stated by Tim Berners-Lee and Nigel Shadbolt in The Times on New Year’s Eve 2011, “One reason that the worldwide web worked was because people reused each other’s content in ways never imagined or achieved by those who created it. The same will be true of open data.”

The examples that follow focus on licensing and reuse of data included with and/or harvestable from journal publications, in the context of the proposed change to BioMed Central’s standard license agreement, above, for open access and open data journal articles.

Example #1 – analysis of a large clinical trial dataset

In April 2011 Sandercock et al., published in Trials ‘The International Stroke Trial Database’ [44], which “aimed to make individual patient data from the International Stroke Trial (IST), one of the largest randomised trials ever conducted in acute stroke, available for public use, to facilitate the planning of future trials and to permit additional secondary analyses.”

The “database”, including 19,000 anonymized individual patient data, is available with the journal article as a 4.3 Mb CSV file (http://www.trialsjournal.com/content/supplementary/1745-6215-12-101-s1.csv webcite) under a CC-BY license. With the new license agreement as proposed in this article the CSV file would be available for reuse without a legal requirement for attribution engrained in CC-BY. Secondary uses for this dataset might include a novel secondary analysis by a different group of researchers, and analysis and integration of the dataset in the context of a systematic review and meta-analysis of randomized trials of heparin and/or aspirin in acute ischemic stroke. Both of these activities might conceivably result in further publications. Although there would be no legal requirement for attribution, for any secondary article about this dataset – or indeed any systematic review – to be scientifically valid it would need to cite its source(s) of data.

Example #2 – Application of magnetic resonance techniques to cross-species comparative studies

Magnetic Resonance Imaging (MRI) techniques are used to better understand the evolution of specific traits in animals and cross-species comparisons (for example in primates) are particularly important. But due to ethical, practical and funding limitations single studies typically are only able to consider one or two species. There is only one publicly available dataset that has brains from multiple primate species scanned according to a common protocol and these scans (of 11 species) were recorded (in vivo) well over a decade ago, and so do not meet the quality criteria that underpin more recent brain morphometric algorithms of the kind required for cross-species studies of brain structure. However, a review of this area of research found that “the major barrier to cross-species MR-based brain morphometry is not the lack of data nor analytical tools but barriers preventing to combine them” [57]. Open data in this field would undoubtedly drive new discoveries.

Example #3 – Research utilizing text and data from journal publications

The copyright status of data obtained though text-mining is debatable. The numerical instances of a particular gene or protein name in a full-text corpus of articles could be valuable for secondary research and, in the US at least, are likely to be considered (non-copyrightable) facts. Some scholars take the position that mining does not violate copyright law because it does not meet the statutory definition of copying which requires “fixing” the work in a permanent form [15]. Yet text mining is often restricted by commercial publishers. In the study of small angle scattering (a “technique based on the deflection of a beam of particles, or an electromagnetic or acoustic wave, away from the straight trajectory after it interacts with structures that are much larger than the wavelength of the radiation” according to Wikipedia [58]), a researcher might be interested to harvest the data used in other publications to test their analysis tools and provide better teaching aids. Generally, in this area of research, the data are only presented as a graph, the data analysis is not spelt out and there is no specific license attached if the data are available (Cameron Neylon, personal communication).

Example #4 – Open bibliography

Online scientific publishing has driven a diversification of measures of research and researcher impact, extending the focus from journal impact factors to article and individually-led metrics. Bibliographic information (rather than copyright attribution), which enables identification of scholarly work and tracks citations to scientists’ work, is central to earning of academic credit for concepts and ideas. Many services are now available which enable individual authors to calculate their citation index, known as the Hirsch or h-index. Examples include Scopus, Thomson ISI, Google Scholar and Microsoft Academic Search. However, much of the data underlying these metrics is not available openly, leading to multiple scores for the same individual or paper – depending on the tool or service used, which have different corpuses and different algorithms for calculating impact scores. A common, open bibliography, as has been established by some leading libraries would enable anyone to assess, utilize and build applications based on the data [59]. And furthermore, from a researcher’s perspective this approach is far more efficient, negating the need to maintain and report multiple sources of data from multiple impact-measuring tools. As outlined by Jones et al.[49] the motivations for and opportunities for open bibliographies are many. The negative implications of open bibliography for an author of a paper are negligible. Under the license agreement proposed in this article CC0 would apply to the article title, author names and information, unique identifying and publishers’ information, and reference list. Given a primary use of bibliographic information is to track scholarly citation activity, authors could reasonably expect these open data to increase the visibility and impact of their work.

Example #5 – Reproduction/validation of results for teaching and further research

In September 2010 Tommi Nyman and colleagues published an article in BMC Evolutionary Biology, “How common is ecological speciation in plant-feeding insects? A 'Higher' Nematinae perspective” [60]. The article included, in addition to the sequence data used to reconstruct the phylogenetic trees, the background data used in the phylogeny-based ecological analyses as additional file 1 – an Excel file. The data are well labeled and readily understandable by other scientists and fully document how they sampled their insects. This informative approach means, for example, readers would not need work through the references to discover the sampling used. These data have potential usage for reproduction and validation of the article’s findings, for teaching purposes, and conceivably uses involving the processing and integration of the data using computer software. Explicit dedication of these data to the public domain minimizes barriers to these scientifically important activities and maximizes the reuse potential of the data, as we could be more confident that all future uses of the data will not be impeded by licensing restrictions.

Concluding remarks – and what next?

Legal issues present substantial barriers, in theory and reality, to the reuse and integration of research data which are free to access online, and data published in peer-reviewed journals. The implementation of a new license and waiver agreement, as per the protocol described in this article, in BioMed Central journals and in the future by other open access publishers should help further realize the benefits of open data for the scientific community – and beyond. We invite all our readers and authors to consider and comment on the implications of the proposed change to BioMed Central’s license agreement set out in this article.

Competing interests

MC is Managing Director of BioMed Central, part of Springer Science + Business Media. IH is an employee of BioMed Central.

Authors' contributions

The idea for this manuscript was conceived at a meeting (reported on the BioMed Central Blog, [13]) which was attended by MC and a number of those listed in the acknowledgements section of this article. The meeting was chaired by IH. IH wrote the first draft of the manuscript. MC was involved in critical review and editing of the original manuscript, and revised versions thereof, in response to comments received from the acknowledged contributors. Both authors read and approved the final manuscript.

Acknowledgements

This article builds on some of the concepts described in BioMed Central’s draft open data statement, and as such a small number of passages from it have been included verbatim in this article.

Thanks to Daniel Mietchen and Cameron Neylon for contributing practical examples of open data in life sciences for this paper.

For their comments on an earlier draft of this paper, thanks to: Victoria Stodden (Columbia University), Diane Cabell (Oxford Internet Institute), Ruth Wilson (Nature Publishing Group), Daniel Mietchen (EvoMRI Communications), Cameron Neylon (Public Library of Science, PLoS), Heather Piwowar (University of British Columbia), Susanna Sansone (University of Oxford), John Wilbanks (Ewing Marion Kauffman Foundation).

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