It is important to note that the survey predated NSF's requirement for data management plans. Beginning on January 18, 2011 proposals submitted to NSF must include a data management plan. At the time of the survey, NSF did not require such plans for its funded projects. When respondents were asked whether their primary funding agency requires them to provide a data management plan, more than half (55%) reported no, 29% yes, and 16% said they do not know.

Respondents reported that they use various types of data in their research, including experimental data, observational data, data models, abiotic surveys, and biotic surveys. Many respondents use more than one data type. The responses for the various types of data used are presented in Table 4 .

Some respondents currently access data from local, national or international networks. The most commonly used are organization-specific systems (39%) and the Long-term Ecological Research Network (LTER) (32%). Respondents could select more than one source (see Table 3 ).

Effective data management and use relies on effective tools. A series of questions about satisfaction with tools for all aspects of the data lifecycle reveal some variation in satisfaction (see Table 6 ). Only about a quarter (26%) of the respondents is satisfied with the tools for preparing metadata, while over 32% are dissatisfied. The large number of respondents who replied that they neither agree nor disagree (42%) could be interpreted in two ways: either they truly are indifferent or they are unsure about what metadata means. There is some reason to believe that the latter is true as nearly half (46%) of the respondents answered “none” to the question “What metadata do you currently use to describe your data?” Forty two percent reported that they are satisfied with the tools for preparing their documentation; however, 31% indicated that they neither agree nor disagree. Clearly, there is room for more effective tools and education as it applies to metadata concepts and principles as a component of data management.

A majority of the respondents are satisfied with their current processes for most of the initial and short-term parts of the research and data lifecycle, including collecting their research data, searching for their data, analyzing their data, and short-term storage of their data. A smaller majority say they are satisfied with cataloging or describing their data (59.8% agree strongly or somewhat). However, the satisfaction rate for the process of storing their data beyond the life of the project (long-term) is much lower than the short-term, only 45% versus 73%. More than a third (35%) of the respondents stated that they are dissatisfied with the long-term storage process (see Table 5 ).

Nearly half (48%) of the respondents reported that their organization or project does not provide the necessary funds to support data management during the life of a research project. More than half (59%) indicated that their organization or the project does not provide training on best practices for data management. Also, 59% of the respondents replied that their organization or project does not provide the necessary funds to support data management beyond the life of the project (see Table 7 ). Institution and Agency initiatives, including efforts like DataONE, can greatly improve these results.

Institutions can help or hinder good data management. Policies and assistance with data management across the data lifecycle vary among institutions. While 43% of the respondents agreed that their organization or project has a formal established process for managing data during the life of the project, almost half (47%) of the respondents disagreed with the statement that their organization or project has a formal established process for storing data beyond the life of the project. Only 38% of the respondents reported that they have a formal established process for storing data long-term, while 45% of the respondents replied that their organization provides, to a degree, the necessary tools and technical support for data management during the life of the project (short-term). Only one third (35%) of the respondents are provided with the necessary tools and technical support for long-term data management.

Adding descriptive metadata to datasets helps makes the dataset more accessible by others and into the future. Respondents were asked to indicate all metadata standards they currently use to describe their data. More than half of the respondents (56%) reported that they did not use any metadata standard and about 22% of respondents indicated they used their own lab metadata standard. This could be interpreted that over 78% of survey respondents either use no metadata or a local home grown metadata approach. Clearly, educational programs including workshops and providing easy tools for metadata training could improve this situation. Awareness of why metadata improves access to data and guidance on standards would both be beneficial. The metadata standards that are used by the participants are presented in Table 9 .

Respondents were asked to indicate whether they have the sole responsibility for approving access to their data. Of those who answered this question, 43% (n = 545) have the sole responsibility for all their datasets, 37% (n = 466) have for some of their datasets, and 21% (n = 266) do not have the sole responsibility.

Respondents were asked their agreement on a five-point scale to a series of statements (see Table 8 ). Nearly two thirds (67%) of the respondents agreed that lack of access to data generated by other researchers or institutions is a major impediment to progress in science. Half (50%) of the respondents reported that lack of access to data generated by other researcher or institution has restricted their ability to answer scientific questions. Three quarters (75%) of the respondents replied that data may be misinterpreted due to complexity of the data across their research field and 71% of the respondents agree that data may be misinterpreted due to poor quality of data across their research field. Nearly three quarters (74%) of the respondents believe that data may be used in other ways than intended across their research field.

We asked respondents about their views on the use of data across their research field. Note that this measures their perceptions or opinions and does not necessarily completely reflect actual practice. Still, the level of agreement or disagreement with these statements reveals many psychological barriers to good data sharing practice.

Data Sharing.

Nearly one third of the respondents chose not to answer whether they make their data available to others. Of those who did respond, 46% reported they do not make their data electronically available to others. Almost as many reported that at least some of their data are available somehow, either on their organization's website, their own website, a national network, a global network, a personal website, or other (see Table 10). The high percentage of non-respondents to this question most likely indicates that data sharing is even lower than the numbers indicate. Furthermore, the less than 6% of scientists who are making “All” of their data available via some mechanism, tends to re-enforce the lack of data sharing within the communities surveyed.

Only about a third (36%) of the respondents agree that others can access their data easily, although three-quarters share their data with others (see Table 11). This shows there is a willingness to share data, but it is difficult to achieve or is done only on request.

Researchers cite many reasons why their data are not available electronically to others (see Table 12). The leading reason is insufficient time (54%), followed by lack of funding (40%). These problems are difficult to solve, but systems that make it quick and easy to share data without additional cost may help. Other reasons such as having no place to put the data (24%), lack of standards (20%), and “sponsor does not require” (17%) may be easier to resolve by subject or government initiatives or large scale projects such as DataONE and other DataNet partners. It is also important to note that only 14% of respondents stated that their data “Should not be Available”, which may bode well for the future of data sharing if logistics are resolved.

Regarding their attitudes towards data sharing, most of the respondents (85%) are interested in using other researchers' datasets, if those datasets are easily accessible. Of course, since only half of the respondents report that they make some of their data available to others and only about a third of them (36%) report their data is easily accessible, there is a major gap evident between desire and current possibility. Seventy-eight percent of the respondents said they are willing to place at least some their data into a central data repository with no restrictions.

Data repositories need to make accommodations for varying levels of security or access restrictions. When asked whether they were willing to place all of their data into a central data repository with no restrictions, 41% of the respondents were not willing to place all of their data. Nearly two thirds of the respondents (65%) reported that they would be more likely to make their data available if they could place conditions on access.

Less than half (45%) of the respondents are satisfied with their ability to integrate data from disparate sources to address research questions, yet 81% of them are willing to share data across a broad group of researchers who use data in different ways.

Along with the ability to place some restrictions on sharing for some of their data, the most important condition for sharing their data is to receive proper citation credit when others use their data. For 92% of the respondents, it is important that their data are cited when used by other researchers. Eighty-six percent of survey respondents also noted that it is appropriate to create new datasets from shared data. Most likely, this response relates directly to the overwhelming response for citing other researchers' data. The breakdown of this section is presented in Table 13.

The participants were asked a series of questions about whether they find it a fair condition for the use of their data when these conditions are met. Afterwards, they were presented with the same conditions and asked whether they find each a fair condition for the use of other people's data. Respondents do not differentiate much between what they consider fair conditions for use of others' data and fair conditions for use of their own data (see Table 14). Sixty-one percent of the respondents find it fair to use other people's data if they give them co-authorship on publications resulting from use of the data. A vast majority (93%) find it a fair condition to use other people's data if there is formal acknowledgement of the data providers and/or funding agencies in all disseminated work making use of the data and 95% of the respondents reported that they find it fair to use other people's data if there is formal citation of the data providers and/or funding agencies in all disseminated work making use of the data. Also, 81% percent of the respondents reported that it is fair to use other people's data if the provider has the opportunity to collaborate on the project (including, for example, consultation on analytic methods, interpretation of results, dissemination of research results, etc.).

A little more than the half (52%) of the respondents believe it is fair to disseminate results based (at least in part) on data without the data provider's approval. The respondents were asked whether it is a fair condition to use other people's data if at least part of the costs of data acquisition, retrieval or provision are recovered. Over two-thirds (69%) of them replied no, either indicating that paying for the costs of data does not include the right to use that data or that they do not believe that data users should be required to pay data creators.

Reviewing derivative works is important to many; 63% agree it is a fair condition to use other people's data if results based (at least in part) on the data are disseminated with the data provider having the opportunity to review, but not approve, the results and make suggestions or comments. In addition, 70% agree it is a fair condition to use other people's data if reprints of articles that make use of the data are provided to the data provider. Sixty-nine percent of the respondents find it fair to use other people's data if the data provider is given a complete list of all products that make use of the data, including articles, presentations, educational materials, etc. Nearly three quarters (72%) of the respondents find it fair to use other people's data if there is mutual agreement on reciprocal sharing of data.

Respondents were asked whether it is fair to use other people's data if legal permission for data use is obtained. This question is perhaps more important for researchers in corporate or other settings, where legal rights to data may be important. Slightly over half (54%) said no, indicating they feel it is not necessary or desirable to obtain legal permission. In another question, approximately two-thirds (67%) find it fair to use other people's data if the data provider is given and agrees to a statement of uses to which the data will be put.