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Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication.
Automated methods identified 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data. Associated datasets in best-practice repositories were found for 25% of these articles, increasing from less than 5% in 2001 to 30%–35% in 2007–2009. Accounting for sensitivity of the automated methods, approximately 45% of recent gene expression studies made their data publicly available.
First-order factor analysis on 124 diverse bibliometric attributes of the data creation articles revealed 15 factors describing authorship, funding, institution, publication, and domain environments. In multivariate regression, authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available.
These results suggest research data sharing levels are still low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let's learn from those with high rates of sharing to embrace the full potential of our research output.
Citation: Piwowar HA (2011) Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data. PLoS ONE 6(7): e18657. https://doi.org/10.1371/journal.pone.0018657
Editor: Cameron Neylon, Science and Technology Facilities Council, United Kingdom
Received: September 16, 2010; Accepted: March 15, 2011; Published: July 13, 2011
Copyright: © 2011 Healther A. Piwowar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was completed in the Department of Biomedical Informatics at the University of Pittsburgh and partially funded by an NIH National Library of Medicine training grant 5 T15 LM007059. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
Sharing and reusing primary research datasets has the potential to increase research efficiency and quality. Raw data can be used to explore related or new hypotheses, particularly when combined with other available datasets. Real data are indispensable for developing and validating study methods, analysis techniques, and software implementations. The larger scientific community also benefits: Sharing data encourages multiple perspectives, helps to identify errors, discourages fraud, is useful for training new researchers, and increases efficient use of funding and population resources by avoiding duplicate data collection.
Eager to realize these benefits, funders, publishers, societies, and individual research groups have developed tools, resources, and policies to encourage investigators to make their data publicly available. For example, some journals require the submission of detailed biomedical datasets to publicly available databases as a condition of publication [1], [2]. Many funders require data sharing plans as a condition of funding: Since 2003, the National Institutes of Health (NIH) in the USA has required a data sharing plan for all large funding grants [3] and has more recently introduced stronger requirements for genome-wide association studies [4]. As of January 2011, the US National Science Foundation requires that data sharing plans accompany all research grant proposals [5]. Several government whitepapers [6], [7] and high-profile editorials [8], [9] call for responsible data sharing and reuse. Large-scale collaborative science is increasing the need to share datasets [10], , and many guidelines, tools, standards, and databases are being developed and maintained to facilitate data sharing and reuse [12], [13].
Despite these investments of time and money, we do not yet understand the impact of these initiatives. There is a well-known adage: You cannot manage what you do not measure. For those with a goal of promoting responsible data sharing, it would be helpful to evaluate the effectiveness of requirements, recommendations, and tools. When data sharing is voluntary, insights could be gained by learning which datasets are shared, on what topics, by whom, and in what locations. When policies make data sharing mandatory, monitoring is useful to understand compliance and unexpected consequences.
Dimensions of data sharing action and intention have been investigated by a variety of studies. Manual annotations and systematic data requests have been used to estimate the frequency of data sharing within biomedicine [14], [15], [16], [17], though few attempts were made to determine patterns of sharing and withholding within these samples. Blumenthal [18], Campbell [19], Hedstrom [20], and others have used survey results to correlate self-reported instances of data sharing and withholding with self-reported attributes like industry involvement, perceived competitiveness, career productivity, and anticipated data sharing costs. Others have used surveys and interviews to analyze opinions about the effectiveness of mandates [21] and the value of various incentives [20], [22], [23], [24]. A few inventories list the data-sharing policies of funders [25], [26] and journals [1], [27], and some work has been done to correlate policy strength with outcome [2], [28]. Surveys and case studies have been used to develop models of information behavior in related domains, including knowledge sharing within an organization [29], [30], physician knowledge sharing in hospitals [31], participation in open source projects [32], academic contributions to institutional archives [33], [34], the choice to publish in open access journals [35], sharing social science datasets [20], and participation in large-scale biomedical research collaborations [36].
Although these studies provide valuable insights and their methods facilitate investigation into an author's intentions and opinions, they have several limitations. First, associations to an investigator's intention to share data do not directly translate to associations with actually sharing data [37]. Second, associations that rely on self-reported data sharing and withholding likely suffer from underreporting and confounding, since people admit withholding data much less frequently than they report having experienced the data withholding of others [18].
I suggest a supplemental approach for investigating research data-sharing behavior. I have collected and analyzed a large set of observed data sharing actions and associated study, investigator, journal, funding, and institutional variables. The reported analysis explores common factors behind these attributes and looks at the association between these factors and data sharing prevalence.
I chose to study data sharing for one particular type of data: biological gene expression microarray intensity values. Microarray studies provide a useful environment for exploring data sharing policies and behaviors. Despite being a rich resource valuable for reuse [38], microarray data are often, but not yet, universally shared. Best-practice guidelines for sharing microarray data are fairly mature [12], [39]. Two centralized databases have emerged as best-practice repositories: the Gene Expression Omnibus (GEO) [13] and ArrayExpress [40]. Finally, high-profile letters have called for strong journal data-sharing policies [41], resulting in unusually strong data sharing requirements in some journals [42]. As such, the results here represent data sharing in an environment where it has been particularly encouraged and supported.
In brief, I used a full-text query to identify a set of studies in which the investigators generated gene expression microarray datasets. Best-practice data repositories were searched for associated datasets. Attributes of the studies were used to derive factors related to the investigators, journals, funding, institutions, and topic of the studies. Associations between these study factors and the frequency of public data archiving were determined through multivariate regression.
Studies for analysis
The set of "gene expression microarray creation" articles was identified by querying the title, abstract, and full-text of PubMed, PubMed Central, Highwire Press, Scirus, and Google Scholar with portal-specific variants of the following query:
("gene expression" [text] AND "microarray" [text] AND "cell" [text] AND "rna" [text])
AND ("rneasy" [text] OR "trizol" [text] OR "real-time pcr" [text])
NOT ("tissue microarray*" [text] OR "cpg island*" [text])
Retrieved articles were mapped to PubMed identifiers whenever possible; the union of the PubMed identifiers returned by the full text portals was used as the definitive list of articles for analysis. An independent evaluation of this approach found that it identified articles that created microarray data with a precision of 90% (95% confidence interval, 86% to 93%) and a recall of 56% (52% to 61%), compared to manual identification of articles that created microarray data [43].
Because Google Scholar only displays the first 1000 results of a query, I was not able to view all of its hits. I tried to identify as many Google Scholar search results as possible by iteratively appending a variety of attributes to the end of the query, including various publisher names, journal title words, and years of publication, thereby retrieving distinct subsets of the results 1000 hits at a time.
Data availability
The dependent variable in this study was whether each gene expression microarray research article had an associated dataset in a best-practice public centralized data repository. A community letter encouraging mandatory archiving in 2004 [41] identified three best-practice repositories for storing gene expression microarray data: NCBI's Gene Expression Omnibus (GEO), EBI's ArrayExpress, and Japan's CIBEX database. The first two were included in this analysis, since CIBEX was defunct until recently.
An earlier evaluation found that querying GEO and ArrayExpress with article PubMed identifiers located a representative 77% of all associated publicly available datasets [44]. I used the same method for finding datasets associated with published articles in this study: I queried GEO for links to the PubMed identifiers in the analysis sample using the "pubmed_gds [filter]" and queried ArrayExpress by searching for each PubMed identifier in a downloaded copy of the ArrayExpress database. Articles linked from a dataset in either of these two centralized repositories were considered to have "shared their data" for the endpoint of this study, and those without such a link were considered not to have shared their data.
Study attributes
For each study article I collected 124 attributes for use as independent variables. The attributes were collected automatically from a wide variety of sources. Basic bibliometric metadata was extracted from the MEDLINE record, including journal, year of publication, number of authors, Medical Subject Heading (MeSH) terms, number of citations from PubMed Central, inclusion in PubMed subsets for cancer, whether the journal is published with an open-access model and if it had data-submission links from Genbank, PDB, and SwissProt.
ISI Journal Impact Factors and associated metrics were extracted from the 2008 ISI Journal Citation Reports. I quantified the content of journal data-sharing policies based on the "Instruction for Authors" for the most commonly occurring journals.
NIH grant details were extracted by cross-referencing grant numbers in the MEDLINE record with the NIH award information (http://report.nih.gov/award/state/state.cfm). From this information I tabulated the amount of total funding received for each of the fiscal years from 2003 to 2008. I also estimated the date of renewal by identifying the most recent year in which a grant number was prefixed by a "1" or "2" —indication that the grant is "new" or "renewed," respectively.
The corresponding address was parsed for institution and country, following the methods of Yu et al. [45]. Institutions were cross-referenced to the SCImago Institutions Rankings 2009 World Report (http://www.scimagoir.com/) to estimate the relative degree of research output and impact of the institutions.
Attributes of study authors were collected for first and last authors (in biomedicine, customarily, the first and last authors make the largest contributions to a study and have the most power in publication decisions). The gender of the first and last authors were estimated using the Baby Name Guesser website (http://www.gpeters.com/names/baby-names.php). A list of prior publications in MEDLINE was extracted from Author-ity clusters, 2009 edition [46], for the first and last author of each article in this study. To limit the impact of extremely large "lumped" clusters that erroneously contain the publications of more than one actual author, I excluded prior publication lists for first or last authors in the largest 2% of clusters and instead considered these data missing. For each paper in an author's publication history with PubMed identifiers numerically less than the PubMed identifier of the paper in question, I queried to find if any of these prior publications had been published in an open source journal, were included in the "gene expression microarray creation" subset themselves, or had reused gene expression data. I recorded the date of the earliest publication by the author and the number of citations to date that their earlier papers received in PubMed Central.
I attempted to estimate if the paper itself reused publicly available gene expression microarray data by looking for its inclusion in the list that GEO keeps of reuse at http://www.ncbi.nlm.nih.gov/projects/geo/info/ucitations.html.
Data collection scripts were coded in Python version 2.5.2 (many libraries were used, including EUtils, BeautifulSoup, pyparsing and nltk [47]) and SQLite version 3.4. Data collection source code is available at github (http://github.com/hpiwowar/pypub).
Statistical methods
Statistical analysis was performed in R version 2.10.1 [48]. P-values were two-tailed. The collected data were visually explored using Mondrian version 1.1 [49] and the Hmisc package [50]. I applied a square-root transformation to variables representing count data to improve their normality prior to calculating correlations.
To calculate variable correlations, I used the hector function in the polycor library. This computes polyserial correlations between pairs of numeric and ordinal variables and polychoric correlations between two ordinal variables. I modified it to calculate Pearson correlations between numeric variables using the rcorr function in the Hmisc library. I used a pairwise-complete approach to missing data and used the nearcor function in the sfsmisc library to make the correlation matrix positive definite. A correlation heatmap was produced using the gplots library.
I used the nFactors library to calculate and display the scree plot for correlations.
Since the correlation matrix was not well-behaved enough for maximum-likelihood factor analysis, first-order exploratory factor analysis was performed with the fa function in the psych library, using the minimum residual (minres) solution and a promax oblique rotation. Second-order factor analysis also used the minres solution but a varimax rotation, since I wanted these factors to be orthogonal. I computed the loadings on the original variables for the second-order factors using the method described by Gorsuch [51].
Before computing the factor scores for the original dataset, missing values were imputed through Gibbs sampling with two iterations through the mice library.
Using this complete dataset, I computed scores for each of the datapoints onto all of the first and second-order factors using Bartlett's algorithm as extracted from the factanal function. I submitted these factor scores to a logistic regression using the lrm function in the rms package. Continuous variables were modeled as cubic splines with 4 knots using the rcs function from the rms package, and all two-way interactions were explored.
Finally, hierarchical supervised clustering on the datapoints was performed to learn which factors were most predictive and then estimated the data sharing prevalence in a contingency table of these two clusters split at their medians.
Full-text queries for articles describing the creation of gene expression microarray datasets returned PubMed identifiers for 11,603 studies.
MEDLINE fields were still "in process" for 512 records, resulting in missing data for MeSH-derived variables. Impact factors were found for all but 1,001 articles. Journal policy variables were missing for 4,107 articles. The institution ranking attributes were missing for 6,185. I cross-referenced NIH grant details for 3,064 studies (some grant numbers could not be parsed, because they were incomplete or strangely formatted). I was able to determine the gender of the first and last authors, based on the forenames in the MEDLINE record, for all but 2,841 first authors and 2,790 last authors. All but 1,765 first authors and 797 last authors were found to have a publication history in the 2009 Author-ity clusters.
PubMed identifiers were found in the "primary citation" field of dataset records in GEO or ArrayExpress for 2,901 of the 11,603 articles in this dataset, indicating that 25% (95% confidence intervals: 24% to 26%) of the studies deposited their data in GEO or ArrayExpress and completed the citation fields with the primary article PubMed identifier. This is my estimate for the prevalence of gene expression microarray data deposited into the two predominant, centralized, publicly accessible databases.
This data-sharing rate increased with each subsequent article publication year, as seen in Figure 1, increasing from less than 5% in 2001 to 30%–35% in 2007–2009. Accounting for the sensitivity of my automated method for detecting open data anywhere on the internet (about 77% [44]), it could be estimated that approximately 45% (0.35/0.77) of recent gene expression studies have made their data publicly available.
The data-sharing rate also varied across journals. Figure 2 shows the data-sharing rate across the 50 journals with the most articles in this study. Many of the other attributes were also associated with the prevalence of data sharing in univariate analysis, as seen in Figures S1, S2, S3, S4, S5.
First-order factors
I tried to use a scree plot to determine the optimal number of factors for first-order analysis. Since the scree plot did not have a clear drop-off, I experimented with a range of factor counts near the optimal coordinates index (as calculated by nScree in the nFactors R-project library) and finalized on 15 factors. The correlation matrix was not sufficiently well-behaved for maximum-likelihood factor analysis, so I used a minimum residual (minres) solution. I chose to rotate the factors with the promax oblique algorithm, because first-order factors were expected to have significant correlations with one another. The rotated first-order factors are given in Table 1 with loadings larger than 0.4 or less than −0.4. Some of the loadings are greater than one. This is not unexpected since the factors are oblique and thus the loadings in the pattern matrix represent regression coefficients rather than correlations. Correlations between attributes and the first-order factors are given in the structure matrix in Table S1. The factors have been named based on the variables they load most heavily, using abbreviations for publishing in an Open Access journal (OA) and previously depositing data in the Gene Expression Omnibus (GEO) or ArrayExpress (AE) databases.
After imputing missing values, I calculated scores for each of the 15 factors for each of the 11,603 data collection studies.
Many of the factor scores demonstrated a correlation with frequency of data sharing in univariate analysis, as seen in Figure 3. Several factors seemed to have a linear relationship with data sharing across their whole range. For example, whereas the data sharing rate was relatively low for studies with the lowest scores on the factor related to the citation and collaboration rate of the corresponding author's institution (in Figure 3, the first row under the heading "Institution high citation & collaboration"), the data sharing rate was higher for studies that scored within the 25th to 50th percentile on that factor, higher still for studies the third quartile, and studies from highly-cited institutions, above the 75th percentile had a relatively high rate of data sharing. A trend in the opposite direction can be seen for the factor "Humans & cancer": the higher a study scored on that factor, the less likely it was to have shared its data.
Percentage of studies with shared data is shown for each quartile for each factor. Univariate analysis.
Most of these factors were significantly associated with data-sharing behavior in a multivariate logistic regression: p = 0.18 for "Large NIH grant", p<0.05 for "No GEO reuse & YES high institution output" and "No K funding or P funding", and p<0.005 for the other first-order factors. The increase in the odds of data sharing is illustrated in Figure 4 as scores on each factor in the model are moved from their 25th percentile value to their 75th percentile value.
Odd ratios are calculated as factor scores are each varied from their 25th percentile value to their 75th percentile value. Horizontal lines show the 95% confidence intervals of the odds ratios.
Second-order factors
The heavy correlations between the first-order factors suggested that second-order factors may be illuminating. Scree plot analysis of the correlations between the first-order factors suggested a solution containing five second-order factors. I calculated the factors using a "varimax" rotation to find orthogonal factors. Loadings on the first-order factors are given in Table 2.
Since interactions make these second-order variables slightly difficult to interpret, I followed the method explained by Gorsuch [51] to calculate the loadings of the second-order variables directly on the original variables. The results are listed in Table 3. I named the second-order factors based on the loadings on the original variables.
I then calculated factor scores for each of these second-order factors using the original attributes of the 11,603 datapoints. In univariate analysis, scores on several of the five factors showed a clear linear relationship with data sharing frequency, as illustrated in Figure 5.
Percentage of studies with shared data is shown for each quartile for each factor. Univariate analysis.
All five of the second-order factors were associated with data sharing in multivariate logistic regression, p<0.001.The increase in odds of data sharing is illustrated in Figure 6 as each factor in the model is moved from its 25th percentile value to its 75th percentile value.
Odd ratios are calculated as factor scores are each varied from their 25th percentile value to their 75th percentile value. Horizontal lines show the 95% confidence intervals of the odds ratios.
Finally, to understand which of these factors was most predictive of data sharing behaviour, I performed supervised hierarchical clustering using the second-order factors. Splits on "OA journal & previous GEO-AE sharing" and "Cancer & Humans" were clearly the most informative, so I simply split these two factors at their medians and looked at the data sharing prevalence. As shown in Table 4, studies that scored high on the "OA journal & previous GEO-AE sharing" factor and low on the "Cancer & Humans" factor were almost three times as likely to share their data as a "Cancer & Humans" study published without a strong "OA journal & previous GEO-AE sharing" environment.
This study explored the association between attributes of a published experiment and the probability that its raw dataset was shared in a publicly accessible database. I found that 25% of studies that performed gene expression microarray experiments have deposited their raw research data in a primary public repository. The proportion of studies that shared their gene expression datasets increased over time, from less than 5% in early years, before mature standards and repositories, to 30%–35% in 2007–2009. This suggests that perhaps 45% of recent gene expression studies have made their data available somewhere on the internet, after accounting for datasets overlooked by the automated methods of discovery [44]. This estimate is consistent with a previous manual inventory [15].
Many factors derived from an experiment's topic, impact, funding, publishing, institutional, and authorship environments were associated with the probability of data sharing. In particular, authors publishing in an open access journal, or with a history of sharing and reusing shared gene expression microarray data, were most likely to share their data, and those studying cancer or human subjects were least likely to share.
It is disheartening to discover that human and cancer studies have particularly low rates of data sharing. These data are surely some of the most valuable for reuse, to confirm, refute, inform and advance bench-to-bedside translational research [52] Further studies are required to understand the interplay of an investigator's motivation, opportunity, and ability to share their raw datasets [53], [54]. In the mean time, we can make some guesses: As is appropriate, concerns about privacy of human subjects' data undoubtedly affect a researcher's willingness and ability (perceived or actual) to share raw study data. I do not presume to recommend a proper balance between privacy and the societal benefit of data sharing, but I will emphasize that researchers should assess the degree of re-identification risk on a study-by-study basis [55], evaluate the risks and benefits across the wide range of stakeholder interests [56], and consider an ethical framework to make these difficult decisions [57]. Learning how to make these decisions well is difficult: it is vital that we educate and mentor both new and experienced researchers in best practices. Given the low risk of re-identification through gene expression microarray data (illustrated by its inclusion in the Open-Access Data Tier at http://target.cancer.gov/dataportal/access/policy.asp), data-sharing rates could also be low for reasons other than privacy. Cancer researchers may perceive their field as particularly competitive, or cancer studies may have relatively strong links to industry – two attributes previously associated with data withholding [58], [59].
NIH funding levels were associated with increased prevalence of data sharing, though the overall probability of sharing remains low even in well-funded studies. Data sharing was infrequent even in studies funded by grants clearly covered by the NIH Data Sharing Policy, such as those that received more than one million dollars per year and were awarded or renewed since 2006. This result is consistent with reports that the NIH Data Sharing Policy is often not taken seriously because compliance is not enforced [54]. It is surprising how infrequently the NIH Data Sharing Policy applies to gene expression microarray studies (19% as per a pilot to this study [60]). The NIH may address these issues soon within its renewed commitment to make data more available [61].
I am intrigued that publishing in an open access journal, previously sharing gene expression data, and previously reusing gene expression data were associated with data sharing outcomes. More research is required to understand the drivers behind this association. Does the factor represent an attitude towards "openness" by the decision-making authors? Does the act of sharing data lower the perceived effort of sharing data again? Does it dispel fears induced by possible negative outcomes from sharing data? To what extent does recognizing the value of shared data through data reuse motivate an author to share his or her own datasets?
People often wonder whether the attitude towards data sharing varies with age. Although I was not able to capture author age, I did estimate the number of years since first and last authors had published their first paper. The analysis suggests that first authors with many years in the field are less likely to share data than those with fewer years of experience, but no such association was found for last authors. More work is needed to confirm this finding given the confounding factor of previous data-sharing experience.
Gene expression publications associated with Stanford University have a very high level of data sharing. The true level of open data archiving is actually much higher than that reflected in this study: Stanford University hosts a public microarray repository, and many articles that did not have a dataset link from GEO or ArrayExpress do mention submission to the Stanford Microarray Database. If one were looking for a community on which to model best practices for data sharing adoption, Stanford would be a great place to start.
Similarly, Physiological Genomics has very high rates of public archiving relative to other journals. Perhaps not coincidentally, to my knowledge Physiological Genomics is the only journal to have published an evaluation of their author's attitudes and experiences following the adoption of new data archiving requirements for gene expression microarray data [21].
Analyzing data sharing through bibliometric and data-mining attributes has several advantages: We can look at a very large set of studies and attributes, our results are not biased by survey response self-selection or reporting bias, and the analysis can be repeated over time with little additional effort.
However, this approach does suffer its own limitations. Filters for identifying microarray creation studies do not have perfect precision, so some non-data-creation studies may be included in the analysis. Because studies that do not create data will not have data deposits, their inclusion alters the composition of what I consider to be studies that create but do not share data. Furthermore, my method for detecting data deposits overlooks data deposits that are missing PubMed identifiers in GEO and ArrayExpress, so the dataset misclassifies some studies that did in fact share their data in these repositories.
I made decisions to facilitate analysis, such as assuming that PubMed identifiers were monotonically increasing with publication date and using the current journal data-sharing policy as a surrogate for the data-sharing policy in place when papers were published. These decisions may have introduced errors.
Missing data may have obscured important information. For example, articles published in journals with policies that I did not examine had a lower rate of data sharing than articles published in journals whose "Instructions to Authors" policies I did quantify. It is likely that a more comprehensive analysis of journal data-sharing policies would provide additional insight. Similarly, the information on funders was limited: I only included funding data on NIH grants. Inclusion of more funders would help us understand the general role of funder policy and funding levels.
Previous work [58] found that investigator gender was correlated with data withholding. It is important to look at gender in multivariate analysis since male scientists are more likely than women to have large NIH grants [62]. Because gender did not contribute heavily to any of the derived factors in this study, additional analysis will be necessary to investigate its association with data sharing behaviour in this dataset. It should be noted that the source of gender data has limitations. The Baby Name Guesser algorithm empirically estimates gender by analyzing popular usage on the internet. Although coverage across names from diverse ethnicities seems quite good, the algorithm is relatively unsuccessful in determining the gender of Asian names. This may have confounded the gender analysis, and the "gender not found" variable might have served as an unexpected proxy for author ethnicity.
The Author-ity system provides accurate author publication histories: A previous evaluation on a different sample found that only 0.5% of publication histories erroneously included more than one author, and about 2% of clusters contained a partial inventory of an author's publication history due to splitting a given author across multiple clusters [46]. However, because the lumping does not occur randomly, my attributes based on author publication histories may have included some bias. For example, the documented tendency of Author-ity to erroneously lump common Japanese names [46] may have confounded the author-history variables with author-ethnicity and thereby influenced the findings on first-author age and experience.
In previous work I used h-index and a-index metrics to measure "author experience" for both the first and last author [60] (in biomedicine, customarily, the first and last authors make the largest contributions to a study and have the most power in publication decisions). A recent paper [63] suggests that a raw count of number of papers and number of citations is functionally equivalent to the h-index and a-index, so I used the raw counts in this study for computational simplicity. Reliance on citations from PubMed Central (to enable scripted data collection) meant that older studies and those published in areas less well represented in PubMed Central were characterized by an artificially low citation count.
The large sample of 11,603 studies captured a fairly diverse and representative subset of gene expression microarray studies, though it is possible that gene expression microarray studies missed by the full-text filter differed in significant ways from those that used mainstream vocabulary to describe their wetlab methods. Selecting a sample based on queries of non-subscription full-text content may have introduced a slight bias towards open access journals. It is worth noting that this study demonstrates the value of open access and open full-text resources for research evaluation.
In regression studies it is important to remember that associations do not imply causation. It is possible, for example, that receiving a high level of NIH funding and deciding to share data are not causally related, but rather result from the exposure and excitement inherent in a "hot" subfield of study.
Importantly, this study did not consider directed sharing, such as peer-to-peer data exchange or sharing within a defined collaboration network, and thus underestimates the amount of data sharing in all its forms. Furthermore, this study underestimated public sharing of gene expression data on the Internet. It did not recognize data listed in journal supplementary information, on lab or personal web sites, or in institutional or specialized repositories (including the well-regarded and well-populated Stanford Microarray Database). Finally, the study methods did not recognize deposits into the Gene Expression Omnibus or ArrayExpress unless the database entry was accompanied by a citation to the research paper, complete with PubMed identifier.
Due to these limitations, care should be taken in interpreting the estimated levels of absolute data sharing and the data-sharing status of any particular study listed in the raw data. More research is needed to attain a deep understanding of information behaviour around research data sharing, its costs and benefits to science, society and individual investigators, and what makes for effective policy.
That said, the results presented here argue for action. Even in a field with mature policies, repositories and standards, research data sharing levels are low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let's learn from those with high rates of sharing and work to embrace the full potential of our research output.
Availability of dataset, statistical scripts, and data collection source code
Raw data and statistical scripts are available in the Dryad data repository at doi:10.5061/dryad.mf1sd [64]. Data collection source code is available at http://github.com/hpiwowar/pypub.
Supporting Information
Figure S1.
Associations between shared data and author attribute variables. Percentage of studies with shared data is shown for each quartile for continuous variables.
Figure S2.
Associations between shared data and journal attribute variables. Percentage of studies with shared data is shown for each quartile for continuous variables.
Figure S3.
Associations between shared data and study attribute variables. Percentage of studies with shared data is shown for each quartile for continuous variables.
Figure S4.
Associations between shared data and funding attribute variables. Percentage of studies with shared data is shown for each quartile for continuous variables.
Figure S5.
Associations between shared data and country and institution attribute variables. Percentage of studies with shared data is shown for each quartile for continuous variables.
Table S1.
Structure matrix with correlations between all attributes and first-order factors.
I sincerely thank my doctoral dissertation advisor, Dr. Wendy Chapman, for her support and feedback.
Author Contributions
Conceived and designed the experiments: HAP. Performed the experiments: HAP. Analyzed the data: HAP. Contributed reagents/materials/analysis tools: HAP. Wrote the paper: HAP.
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Hedstrom M (2006) Producing Archive-Ready Datasets: Compliance, Incentives, and Motivation. IASSIST Conference 2006: Presentations.
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Giordano R (2007) The Scientist: Secretive, Selfish, or Reticent? A Social Network Analysis. E-Social Science.
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Hedstrom M, Niu J (2008) Research Forum Presentation: Incentives to Create "Archive-Ready" Data: Implications for Archives and Records Management. Society of American Archivists Annual Meeting.
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Piwowar HA (2010) Foundational studies for measuring the impact, prevalence, and patterns of publicly sharing biomedical research data. Doctoral Dissertation: University of Pittsburgh.
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Vickers AJ (2008 January 22) Cancer Data? Sorry, Can't Have It. The New York Times.
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Tucker J (2009) Motivating Subjects: Data Sharing in Cancer Research [PhD dissertation.]. Virginia Polytechnic Institute and State University.
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Wellcome Trust (2010) Sharing research data to improve public health: full joint statement by funders of health research.
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Hosek SD, Cox AG, Ghosh-Dastidar B, Kofner A, Ramphal N, et al. (2005) Gender Differences in Major Federal External Grant Programs. RAND Corporation.
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- 64.
Piwowar HA (2011) Data From: Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data. Dryad Digital Repository. Available doi:10.5061/dryad.mf1sd.


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subset: commoncrawl


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When Vladimir Putin came to power in 1999, he immediately called on the Russian military.
"This was really his first act, to launch the war in Chechnya," said Angela Stent, who is director of Georgetown University's Center for Eurasian, Russian and East European Studies and who has met with Putin at an annual conference throughout his presidency.
The Russian troops eventually subdued the rebel fighters in Chechnya, a region inside southern Russia. Over the years, Putin has sent Russian forces on several combat missions abroad, including to the former Soviet republics of Georgia in 2008 and Ukraine in 2014, as well as to Syria in 2015.
Putin's military moves have brought sanctions and ostracization from the West. The Russian economy is weak, and Russia is often described as a "declining power."
But Putin, who has now been president or prime minister for 22 years, strongly disagrees. Russia's military remains a potent force, and Putin has repeatedly used it — or threatened to use it — to demonstrate that Russia is still a country to be reckoned with.
Russia has now massed some 100,000 troops on its border with Ukraine, though it's not clear whether Putin is planning another invasion of Ukraine or is just bluffing as a way to win political concessions.
Given his track record, though, Putin is being taken seriously.
"The Kremlin will stop only when it is stopped," said Ben Hodges, who was the commander of the U.S. Army in Europe when he retired in 2017 as a lieutenant general.
Hodges, who currently lives in Germany, has been observing the Soviet and Russian militaries since he was sent to Europe as a young Army officer in the 1980s.
"They haven't given up one inch," he said of Russia's military actions in recent years. "They've only continued to expand. Sometimes they use actual force. Sometimes they use the threat of force. It seems like we are always on the back foot, that they always have the leverage. We're always responding to them."
This month marks 30 years since the breakup of the Soviet Union, an event Putin has described as the "biggest geopolitical catastrophe" of the 20th century.
Stent says this drives Putin to pursue two clear goals: first, his insistence that Russia be treated as a major global power and, second, his opposition to Western influence, including NATO expansion, into what he considers his neighborhood.
"Putin has been determined, since he became president, not necessarily to restore the Soviet Union, but to get the West to understand and to accept the fact that this is a Russian sphere of influence," Stent said.
Putin likes to keep his opponents guessing. Last April, Russia massed troops on its border with Ukraine. Putin didn't invade, but the episode led to a summit between Putin and President Biden in Geneva in June.
Now, Biden is warning of harsh financial sanctions if Russia invades. Ukrainian President Volodymyr Zelensky says his country's military is much stronger than it was when Russia invaded in 2014, in part because of increased Western military assistance.
So what's the likely outcome this time?
Russian leaders say their country has no plans to invade and is free to place its troops anywhere it likes on Russian soil. But the rhetoric is running hot, and the tense conditions raise the risk of miscalculation.
"This all might be bluster on the part of Moscow, Washington and Kyiv, but all sides seem to have really dug in their heels," said Tim Frye, a Columbia University professor and the author of the book Weak Strongman: The Limits of Power in Putin's Russia.
Frye distinguishes between what were two separate Russian military operations in Ukraine in 2014.
In the first, Russia seized the peninsula of Crimea quickly and without any serious fighting. Russia remains in full control, though its annexation of Crimea has not been recognized internationally.
"Certainly one of the reasons why the Crimean annexation was so popular [in Russia] is that it was largely bloodless. It was a low-risk, high-reward operation," Frye said.
In the second, which happened just weeks later in 2014, Russia supported separatists fighting in eastern Ukraine. That conflict has turned into a stalemate, though sporadic skirmishes still break out, and some 14,000 people have been killed.
"Fighting in eastern Ukraine is something that is much more costly for the Kremlin. There's much less domestic support for this kind of fighting," said Frye.
He believes Putin would likely settle for a negotiated deal that gives him strong political influence in Ukraine — and doesn't involve an invasion.
Negotiations among the U.S., Russia and others are planned for the coming days. But there's no guarantee Putin will win concessions, and his aggressive actions have turned most Ukrainians against Russia.
"If he doesn't realize that he has really alienated the Ukrainian population by doing all of this, then he's really not getting very good feedback," said Stent.
Hodges says even though the Soviet Union may be long gone, many Russians still share Putin's belief that they should have strong influence in former Soviet states.
"If President Putin fell off his horse tomorrow, we would still be dealing with this for years to come," said Hodges. "So this is not just about him."
But for now, everyone is watching Putin to see what he does next.
Greg Myre is an NPR national security correspondent who was based in Russia from 1996 to 1999. Follow him on Twitter: @gregmyre1.
Copyright 2022 NPR. To see more, visit https://www.npr.org.


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We share a common purpose with our partners in health and social care – and that is to provide high quality care and ensure the best possible outcomes for the people who use our services. Promoting improvement is at the heart of what we do.
We endeavour to provide a first class service at all times but sometimes things go wrong and our service may fall below our expected levels.
We pledge to:
- Have a culture of openness and honesty at all levels
- Inform patients in a timely manner when safety incidents have occurred which may affect them
- Provide a written and truthful account of the incident, explaining any investigations and enquiries made
- Provide a written apology
- Provide support if you are affected directly by an incident.
Information leaflets
If you require further information concerning our Duty of Candour procedures please contact the Practice.


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November presents some clear, cool nights for many locations (between winter storms). So bundle up and get outside for stargazing fun!
Milky Way-In early November, stretching from overhead to the southwestern horizon, you can still catch bright portions of the Milky Way. As the month goes on and Earth marches in its orbit around the Sun, the Milky Way will appear lower and lower to the western horizon. The Milky Way is a wonderful target to explore with 50mm or larger binoculars or a wide-field telescope; there are dozens of star clusters to pick out, even in moderately light polluted areas.
Big and Bright Jupiter-In Early November go outside about 9pm on a clear night. Face east and look about half way up towards the zenith, which is directly overhead. Here you'll easily see the bright and big planet Jupiter. Use a telescope to enjoy views of its four brightest moons (Io, Europa, Ganymede, and Callisto) and its striking equatorial cloud belts. A 3" or larger telescope will give rewarding views.
Fire in the Sky-On Saturday night, November 17 the Moon is only a few days old so the conditions are good for the late night appearance of the Leonids Meteor Shower. This shower is predicted to peak on the night of November 17 (and the morning of the 18th) and will have dozens of meteors per hour visible from a dark sky location. The Leonid shower is known for its bright meteors, the brightest of which leave a trail high in the sky that can take several seconds or longer to dissipate.
Best Galaxy Ever!-M31, The Andromeda Galaxy. Around 9pm in early November, M31 lies just north of the stars that form the constellation of Andromedea/NE of Pegasas. Use a star chart, planisphere, or your Orion StarSeek app to help find it with binoculars. You can take a better look with a telescope using a wide angle eyepiece. From a dark sky site this galaxy is an easy naked-eye object as well; Andromeda is the closest large galaxy to our own Milky Way, about 2 million light years away.
A Bright Spot in the Milky Way-High in the northern sky at 10pm is a brighter knot in the Milky Way, between the constellations of Perseus and Cassiopeia. With binoculars you can tell that it is really two open star clusters side by side, the famous Double Cluster in Perseus. Also called NGC 884 & NGC 889, these clusters are relatively close to Earth, about 7-8,000 light years away. They're also very young clusters. Astronomers believe these are only about 3-5 million years old, just "babies" on the cosmic timescale!
A Dark Sky Test-On the opposite side of Andromeda is another nearby galaxy, M33. Use a star chart to look for it in 50mm or larger binoculars. If you have a dark sky to observe from, you can even detect this galaxy with the unaided eye. In fact, M33 is used as a test by many experienced observers to judge the darkness and transparency of a potential observing site.
Catch a Dying Star-High in the western sky, early in the evening, the constellation of Cygnus is still prominent and topped off by the star Deneb at the top of the "Northern Cross". With a star chart, track down the Veil Nebula on the eastern side of Cygnus near the star 52 Cygni. Use an Oxygen III filter and low power while you scan for this object. The Veil is a remnant of a supernova explosion, where a star has died. We recommend a 4" or larger telescope to catch it (but it has been seen in smaller scopes from good dark sky locations).
November's Challenge Object-The Silver Dollar Galaxy, NGC 253. At 9pm, low in the south is the "starburst" galaxy NGC 253. From a dark sky, this grand galaxy is easy to see in binoculars and with a telescope, but can you see it with your naked eye? If you can see this challenging object without a telescope, you definitely have excellent skies (dark and transparent) and you are probably looking at the most distant object that a person can see without a telescope, about 11.5 million light years distant from Earth.
All objects described above can easily be seen with the suggested equipment from a dark sky site, a viewing location some distance away from city lights where light pollution and when bright moonlight does not overpower the stars. All objects have been verified by actual observations by Orion Telescopes & Binoculars Staff at Fremont Peak State Park, and/or Deep Sky Ranch, 60 miles and 90 miles respectively from San Jose International Airport, San Jose, CA.


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More Than 300 Opponents Arested on International Human Rights Day
Posted on December 12, 2013
HAVANA, Dec. 11, 2013 , Let's Talk Press / www.cubanet.org.- Repressive
forces of Raul Castro's military regime arrested several correspondents
from the Hablemos (We Speak) Press Information Center who were trying to
take pictures of the repression against activists and opponents gathered
on Tuesday, 10 December to conduct activities for the International Day
of Human Rights.
Among the correspondents detained, now released, are Magaly Norvis Otero
, Tamara Rodriguez, William Cacer, Ignacio González, Jorge Alberto
Liriano and Roberto de Jesús Guerra, arrested in different areas of the
The arrests, which lasted between five and seven hours, were carried out
by officials of the Department of State Security (DSE ), the political
police, the Directorate General of the National Revolutionary Police
(PNR ) and the General Directorate of Counter Intelligence, all from the
Ministry of the Interior.
Rodriguez, Cacer and Gonzalez were arrested near Coppelia Ice Cream,
located on 23rd Street and L in El Vedado. At this site, Berta Soler,
spokeswoman for the Ladies in White Movement, had called for her group
to stage "a demonstration to the world that the military regime of
President General Raúl Castro does not respect human rights."
Otero and Guerra were arrested at noon Tuesday in Infanta Street at the
corner of Santa Marta, Central Havana, coming out of the headquarters of
Hablemos Press, which remained besieged by agents of the political
police from the early hours of the morning.
The correspondent reported that Jorge Alberto Liriano was arrested in
the town Santiago de las Vegas, Rancho Boyeros municipality, Havana, on
traveling to the center of the capital, and he affirmed that he was
beaten by the security agent known as Gaston.
The correspondents detainees were transferred to the 4th Unit of the
National Police in the Cerro municipality in Havana, and others to the
Detention Center known as Vivac, also in the capital. State Security
Agents and the People's Revolutionary Police (PNR) violently prevented
correspondents Raúl Ramírez Puig and Odalys Pérez Valdes from Guines,
Mayabeque Province, from leaving their homes to travel to Havana, Puig
reported in a phone conversation.
Meanwhile correspondents in Sancti Spiritu, Guantánamo and Holguín
reported harassment and repression suffered this day when the world
celebrated the International Day of Human Rights.
A correspondent at Estado de Sats
Pablo Morales, Hablemos Press correspondent in Havana, said Tuesday
night that by managed to enter the house that is the site of the Estado
de Sats Project, located at No. 4606 1st Avenue between Calles 46 and
60, in Miramar Playa Municipality where activists and organizers
performed different activities for the 65th Anniversary of the Universal
Declaration of Human Rights, as they were repudiated from outside the
housing by about 400 supporters of the Castro regime.
Morales said that, on Tuesday morning, "a dozen activists were violently
arrested by the political police while trying to enter Estado de Sats,"
which remained under siege by police officers, State Security and Rapid
Response Brigades (BRR).
We still do not have an exact figure of how many people were detained
Tuesday. It is estimated to be over 300.
The repressors also cut phone service to dissidents to block communications.
Soler and some 80 Ladies in White in Havana were arrested and beaten,
along with dozens of activists and opponents who tried to gather at
Coppelia Ice Cream.
"Cultural activities"
The authorities of the regime organized youth activities in the same
places that the opposition had gathered, for the purpose of repudiating
In addition to repression in Havana, dozens of activists, opponents and
dissidents were arrested and beaten in Pinar del Rio, Matanzas, Santa
Clara, Cienfuegos Camaguey, Holguin and Santiago de Cuba provinces,
according to reports from independent journalists.
In the town of Velasco, Holguin province, BRR members stormed the home
of Damaris Garcia located at 2501 No. 29th Street between Calle 23 and
32, Manuel Fajardo neighborhood, where 31 opponents had gathered.
"They have been hitting us, throwing stones, sticks, paint and even
beating the children who are here with us," said Garcia and the activist
Rafael Friman.
However, a report published Tuesday by the official Communist Party
newspaper Granma said that "There has never been a single case of
murder, torture or extrajudicial execution has never been a death squad
or an Operation Condor" on the island.
Cubanet, 12 December 2013
Source: "More Than 300 Opponents Arested on International Human Rights
Day | Translating Cuba" -
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5. Visa Invitation Letters
The Organizer issues invitation letter to exhibitors for applying visa upon request.


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Much better than a hollow chocolate bunny, my metaphorical Easter
basket this year held a review copy of Connie Malamed's wonderful new Visual Design Solutions: Principles and Creative Inspiration for Learning.
As a career-long government employee I'm always interested in low-cost solutions so
appreciate Connie's attention to the idea that good design is not necessarily
about money or software. She offers examples created with PowerPoint, tips for taking
your own photographs, and ideas for making something better by, for instance, mixing
photographs for a more complete effect. My other career challenge, again a product of
my government environment, is the unending demand for learning experiences
around deadly-dry-content areas like policy and compliance. Malamed helps here, too, with ideas for making
content more exciting and offering suggestions for challenges like working with
numbers. Some other highlights:
- Visual fluency and the role of symbol in
developing a common language
Overcoming simple challenges that often bedevil
new designers, like working with gradient backgrounds
Alternatives to bullets and other layout challenges
Grouping to support the brain's gift for pattern sensing
Alternatives to bullets and other layout challenges
Grouping to support the brain's gift for pattern sensing
Techniques for creating emphasis
If there's a central message, though, it's the idea of
designing with intention. As I like to
say in one of my own design workshops: "Put your hands in the air and step away
from the computer." Think about the look
and feel and the feeling and the view from 10,000 feet. What is the whole experience you're after? It
is hard, looking at an authoring tool, to refrain from wanting to start loading
content and searching for templates and images. Malamed wants the learner to
have an elegant, complete experience. To
that end she focuses on the view of a project as more than the sum of its
parts. Typeface matters: Even people not
trained in design "pick up cues from a typeface and ascribe its characteristics
to a personality" and are aware when the typeface doesn't match the
message. Color matters: It conveys mood
and stirs emotions, especially pleasure. The palette has psychological impact. The tone of the writing matters. The choice of
when and how to, or not to, use white space matters. In other words: Everything
Malamed's Visual
Design Solutions is an excellent resource useful for anyone in the
training/elearning design/presentation business but also anyone involved in design in general and communication in particular.


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South Korea has been out in front of the coronavirus, limiting it with early, widespread testing and quarantining.
It follows that the country would be among the first to play ball again.
The Korean Baseball Organization hopes this week's start of preseason games leads to a smooth opening of the regular season early next month.
But there will be nothing regular about baseball in front of empty seats. One of the precautions the KBO's taking is playing games with no fans in attendance. Korean teams have experienced a little of it already in intrasquad games.
And some don't like it.
"It was so quiet and boring," says Dan Straily, an American-born pitcher with the Lotte Giants of the KBO. "I had to ask people in the front office to get some music between innings. [To] somehow try to get a little energy [in the stadium]."
In eight Major League seasons, Straily played in stadiums that felt empty. "Occasional weekday games in Oakland come to mind," he says with a laugh. But his first year with the Giants will be the first time truly without any fans, to provide that extra motivation.
"Even if you're on the other side and they're yelling at you, you just feel the energy," he says. "Somewhere deep down, it adds adrenaline to you and you feed off of that."
Will it affect performances on the field?
"We'll find out soon," Straily says. The pitcher in him adds, "my hope is it'll be a little less energy for the hitters, you know, no one to flip their bats for, so we can bring them down a notch with these empty stadiums here."
Epic bat flips are legendary in South Korean baseball; so are the fans, whose organized cheers and energy create game-long parties in the stands.
They will be missed. But banning supporters keeps them safe.
What about those at the ballpark?
The KBO'S new COVID-19 rules include: players having their temperatures checked twice a day; everyone not in a baseball uniform, including umpires and athletic trainers, wearing face masks and gloves; if a player shows symptoms, he'll be immediately quarantined and they'll close the stadium where he played his most recent game; if he tests positive for the virus, contact tracing will figure out others who need to be quarantined for two weeks.
Straily applauds all the rules, except one. A ban on spitting.
"I want someone to find me a game in history where baseball players did not spit on the field," Straily says.
But this pandemic is changing everything, including one of baseball's oldest and, pretty safe to say, most disgusting traditions. Although Straily thinks the spittle prohibition will be hard to enforce.
"I've never been to the baseball field and thought to myself, like, 'OK don't spit,' " he says. "It doesn't make sense. I don't know why we all do it, it's just like one of those things that happens."
Like it or not, South Korea is embarking on a season without spitting or fans, while U.S. leagues watch and discuss possible scenarios of their own.
Baseball has gotten a recent boost from the doctor most associated with the fight against the pandemic in this country. Anthony Fauci says baseball can happen, even with spectators in the stands.
"[You could] limit the amount of people in a stadium," he said on the Yes Network, "and make sure you seat them in a way where they are really quite separated. And maybe even wearing the facial covers, a mask."
But Fauci says it's more likely there'll be a version of baseball with no spectators, "television baseball," like the Korean model. With eyes trained on South Korea, Major League Baseball has been discussing a similar idea, including no fans, close monitoring of players with regular temperature checks, limited travel — in Arizona, where all 30 teams would play the season in a sort of bubble in the Phoenix area.
Meanwhile, in Busan, South Korea, Straily says he's grateful to be playing baseball.
"Just the fact that we get to be starting our season," he says, "show us that things are trending the right way in this country."
Straily has been talking to friends who are Major League players, and he says the situation [in the U.S.] is wearing on a lot of them.
"We're so used to routine as ballplayers, it's one of the things we thrive on — schedules and routines," he says. "To not have those, to have unknowns? That's scary."
He asks those friends what they're hearing about a Major League restart, and they say "nothing." They're just waiting with everyone else and hoping to get going.
And staying ready. All pro athletes live in a state of readiness. Their careers depend on it.
Which is why, Straily says, even with the uncertainty of a pandemic, you can't ever think the season is over. Until someone tells you it's over.
Copyright 2020 NPR. To see more, visit https://www.npr.org.


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