25 Years of Molecular Biology Databases: A Study of Proliferation, Impact, and Maintenance

Introduction

In the past 25 years online database technologies, and especially web-available databases, have transformed scientists' use of research data¹. Online resources offer researchers unencumbered access to digital content regardless of who they are, where they are, or when they are working. While easy access is undeniably powerful, the sustainability of proliferating scientific resources comes with many challenges. The costs of computation and storage have decreased steadily for decades and are no longer rate-limiting factors. However, database providers are still challenged to acquire funds—both initial and ongoing—to cover staff and other technology costs, including efforts to find and retain skilled professionals, update and secure systems, and ensure data are accurate and current. In light of these challenges, sustainability becomes a key concern as online resources and data proliferate (Kalumbi and Ellis, 1998; Guthrie et al., 2008; Ember and Hanisch, 2013).

The recent intensified interest in data, including data science, big data, and data analytics, has been coupled with a push by funding agencies, publishers, and scientists themselves for increased access to research data. A memo from the Office of Science and Technology Policy explicitly called for public access to the products of federally funded research, including data (Holdren, 2013). Sharing data via web-available databases is a common mechanism, yet this creates a conundrum: how can more access to data be attained when there is already intense competition for funding to maintain current databases? A better understanding of the scientific community's capacity to develop and maintain web-available databases would help our understanding of the sustainability of such resources.

To date, database studies have tended to fall on either end of a spectrum. On one end, studies have covered small- or medium-sized samples, allowing careful inspection of individual resources (Marcial and Hemminger, 2010; Kirlew, 2011; Attwood et al., 2015). At the other extreme, studies have evaluated Scientific Data Analysis Resources (SDARs) by proxy through mining tens of thousands of URLs in the academic literature (Wren et al., 2017). Both strategies have provided valuable insights, but studies that have straddled the middle are rarer. This work aims to complement these efforts by gathering a historical sampling of databases that is large enough to evaluate trends in proliferation and impact, but also granular enough to allow identification of sizable subsets of individual databases to assess for maintenance. An overarching goal of this study is to provide the ability to identify subsets of databases for further analysis, both as presented within this study and through subsequent use of the openly released dataset.

Absent the ability to evaluate all online databases across all academic disciplines, a large, diverse, and well-documented sampling of databases is required. Molecular biology is a far-reaching field where principles and techniques are core to nearly all other disciplines in the life sciences, from immunology to biophysics. Correspondingly, molecular biology databases cover a wide swath of physical, chemical, and biological phenomenon across a range of organisms (Fernández-Suárez and Galperin, 2013) and are often created with the intent to be useful to a variety of biologists (Galperin and Cochrane, 2009). Additionally, the molecular biology community has progressively created standards, technology, and community norms to support wide-spread reuse of data.

The desire to develop and maintain community resources is not unique to molecular biology; many other disciplines—ranging from astronomy to hydrology to archeology—have a high appetite for community resources, but as academic endeavors all are similarly constrained by the funding options available. Sustainability of data resources has been a topic of active discussion for many years and continues to be an unresolved issue (OECD, 2017). However, the long and enterprising history of database adoption in molecular biology provides a wealth of diverse databases with mature lifecycles to study. Some are under the purview of large, well-established government organizations, such as ArrayExpress from EMBL-EBI, and others are created by small university-based research groups such as RDP: Ribosomal Database Project. Especially as a population, these databases' lifecycles provide an opportunity to examine the variable conditions under which online databases are created, utilized, and maintained. Given the underlying similarities associated with the challenges of database sustainability across all academic disciplines, lessons gleaned from these molecular biology and bioinformatic resources are likely informative for other domains.

With this in mind, the study presented here aimed to create a census of sufficiently documented molecular biology databases to answer several preliminary research questions and serve as fodder for future work. Namely, the questions addressed herein include: (1) what is the historical rate of database proliferation vs. rate of database attrition? (2) to what extent do citations indicate persistence? and (3) are databases under active maintenance and does evidence of maintenance likewise correlate to citation? Articles published in the annual Nucleic Acids Research (NAR) “Database Issues” were used to identify a population of databases for study. NAR is a prominent, well-respected journal in the molecular biology and bioinformatics communities that has long served as a vehicle for description of data and databases. NAR published articles related to compilations of data, especially gene sequences, for many years, but it wasn't until 1991 that a dedicated supplemental NAR issue focused on databases (Fernández-Suárez and Galperin, 2013). The first formally named Database Issue was published in 1993, but for the purpose of capturing these earlier efforts, the study here includes these two preceding supplemental issues from 1991 and 1992 to provide a fuller history of database development. This affords an exceptionally long period of time given the pace of technology change and development of science itself.

To make use of this 25 year history, the author mined the articles published within these issues to answer fundamental questions about the databases reported. Subsequently, this work represents the identification of 1,727 unique databases and the collection and analysis of descriptive statistics for each, including year debuted in a NAR Database Issue, current availability, as well as article and citation counts for all associated NAR Database Issue publications for each unique database. Additionally, to address gaps in our current knowledge, the apparent date of last update was also determined through visual inspection of web pages for all databases currently available. While assessment of URL response provides a binary understanding of database availability, e.g. either “accessible” or “not accessible,” the apparent date of last update is an important but previously unreported metric that provides a window into the activeness of a database's current operation.

Methods

Dataset Assembly

The initial source of metadata for each database article, including Author, Title, Year, Cited by, and DOI, was gathered from the Scopus abstract and citation database on December 6, 2016. For the purposes of this study, the issues included as “NAR Database Issues” include the formal Database Issues from 1993 forward as well as two preceding supplemental issues in 1991 (Vol. 19, Issue suppl) and 1992 (Vol. 20, Issue suppl). No Database Issue was published in 1995, resulting in a total of 25 issues subjected to query.

Only articles that describe a specific resource were included in analysis. Of the 3,115 records extracted from Scopus for NAR Database Issues, 3,055 articles were included. The articles removed include editorials (n = 10) and overall descriptions of resources at the National Center for Biotechnology Information (n = 17) and the European Bioinformatics Institute (n = 7); both of which commonly mention a dozen or more resources, including non-databases. Articles describing generic topics such as Oracle, Wikipedia, legal interoperability, and regular expressions (n = 4) were also removed. Finally, a small number of duplications and other indexing errors (n = 22) were also discovered and removed, leaving a final count of 3,055 articles in the sample.

Identification of Unique Databases

Unique databases published in NAR Database Issues were identified from individual articles. In 2009 article titles were standardized to “Database Name: short description,” and database names were readily extracted using this syntax. However, previous years offered no such standardization, and titles required manual review. When a name could not be identified from the title, the abstract was reviewed. If this failed, the articles themselves were reviewed. While the vast majority of articles describe a single resource, a small portion (n = 37) were found to describe two or more resources. Assuming equal representation, citations for these articles were divided by the number of resources within to provide a citation count for each database described.

Articles fall into two categories: “debut” articles which cover a database's first appearance in an NAR Database Issue and “update” articles which provide an update of a database previously described in an NAR Database Issue. A “debut” year is not necessarily a creation year since a database may have existed for several years prior to publication. While annual updates were common in early years, submission of update articles were later limited to every other year, with a few exceptions for major resources such as GenBank, DDBJ: DNA Data Bank of Japan, and ENA: European Nucleotide Archive (Galperin and Cochrane, 2009). With the expectation that a given database may be associated with multiple articles over the years, the next step was to determine how many articles describe each unique database. This enables a fuller understanding of a database's history through determination of metrics over multiple years. In order to accomplish this, extracted database names were checked for standardization and then cross-checked to ensure that a single unique database was not misidentified as two (or more) separate databases through cryptic reference in separate articles. Confounding issues complicate this task such as the names of databases changing over the years or mergers between resources. A set of criteria were defined to guide name standardization (Imker, 2018), and for additional validation URLs and author names were also checked to identify potentially missed matches.

Database Availability and Updating

Only web-available databases (n = 1,714) were assessed for availability. Database URLs were recorded from article abstracts and tested manually once between the period of Dec 19, 2016–Feb 22, 2017 to inspect evidence of ongoing updating and maintenance. For databases with more than one URL, either as a result of multiple articles or multiple access points (e.g., mirror sites), only one functional URL had to direct to the database in order for the database to be categorized as available for this study. In some cases, URLs were not functional and returned typical client or server errors, e.g., 403 Forbidden, 404 Not Found, etc. However, HTTP status messages were not uniformly reliable since in other cases the HTTP status message returned code 200 OK, indicating a functional website, yet the website did not provide database access. Examples include webpages that contain a discontinued notice or a redirect either to a related, but generic website (e.g., the home page of a university) or to an entirely unrelated website (e.g., an e-commerce site). In these cases, although these URLs technically resolve, these were recorded as the database being unavailable if no other URLs resolved to the actual database.

For URLs that did resolve appropriately, the websites were inspected for evidence of database upkeep and maintenance. Since there is no standard reporting mechanism, maintenance was inferred through observation of a visible update date on the database's website. Specifically, the home page was scanned, and any areas or navigational elements labeled as “announcements,” “news,” “updates,” “versions,” or “releases” were inspected and the year of the update was recorded, if found. Date stamps for page updates were not counted since these can be auto-generated every time a webpage is accessed. Likewise, copyright dates in footers can be dynamically updated and were not counted as evidence of active maintenance. A database for which no date could be located does not necessarily indicate the database is not being maintained, simply that the date of last update could not be determined. From this analysis a date was located for a total of 591 databases covering the entire 1991–2016 time frame.

Validation and Assignment of Identifiers

Identifiers were assigned to standardized database names to facilitate analysis. As another check of accuracy and also to enhance future usability of the dataset, identified databases were mapped to those found in the Molecular Biology Database Collection (MBDC). The MBDC is hosted on the NAR website at the Oxford University Press and is updated annually to provide researchers with a compendium of current databases (Rigden et al., 2016). Databases within the MBDC collection are assigned a numeric identifier referred to here as MBDC IDs. These identifiers and associated database names were collected from the MBDC website on December 8, 2016. Databases within the MBDC were matched with the standardized database names assigned in this study; when no match was recognized or resources were named ambiguously, the MBDC page was accessed to determine the associated article. As expected, not all MBDC databases correspond to articles published within in NAR Database Issues since the MBDC accepts database submissions without an associated NAR publication. Likewise, not all databases identified in this study are represented in the MBDC since the MBDC is meant to represent a current list of active databases and defunct databases are culled from the collection on an annual basis. A total of 322 databases could not be mapped to MBDC ID, leaving 1,405 that map to the MBDC. The 322 that could not be mapped were given an identifier unique to this study.

Assignment to Citation Quartiles

Two major issues that can skew citation analysis include (1) the number of article citations strongly correlates with time since publication resulting in disproportionately low citation counts for new articles and (2) raw citation counts fluctuate wildly and are difficult to compare side-by-side. To address the first issue, publications from years 2013 to 2016 were excluded from citation analysis based on deviation from average citations/article seen over the preceding period (see Supplementary Figure 1). To address the second issue, percent rankings were adopted as a method to normalize citations (Waltman and Schreiber, 2013). In this analysis, each article's ranked placement within an issue (“Issue Percent Rank”) was calculated by ordering articles by number of citations and determining each article's percentile. For databases with a single debut article published prior to 2013 (n = 842), this represents the database's overall Percent Ranking. For databases with a debut article plus additional subsequent update articles published prior to 2013 (n = 515), the Issue Percent Rank was averaged across all articles to determine the database's overall Percent Ranking. With this calculated, each database was binned into its corresponding Citation Quartile; e.g., databases with a Percent Ranking between 1.0 and 0.75 assigned to the first quartile, and so forth.

Data Analysis

Since the dataset was initially arranged by individual article, the data were subsequently reshaped in the statistical programming language R 3.3.3 to obtain aggregate issue-level, article-level, and database-level metrics. Transformations were performed using the tidyverse “core,” namely the package dplyr_0.7.4 (Wickham et al., 2017), through simplified attachment via the tidyverse_1.1.1 “meta” package (Wickham, 2017b). Analyses were performed using base functions in R as well as the package stringr_1.2.0 (Wickham, 2017a). Results were visualized using the packages ggplot2_2.2.1 (Wickham, 2009) and ggpmisc_0.2.16 (Aphalo, 2016) using the Tol technical specifications for color schemes (Tol, 2012). The explicit steps used for reshaping and analysis are detailed in the documentation provided with the R scripts and data files in the associated openly available dataset (Imker, 2018). To assess potential statistical significance of increasing or decreasing trends for quartiles, a chi-squared test for trends in proportions was used (Dalgaard, 2008). To assess the distribution within quartiles Pearson's chi-squared test for count data was used (Agresti, 2007). Data for trend lines were selected to maximize linearity as determined by calculating the second derivative to identify data points associated with maximum curvature.

Results and Discussion

Growth for NAR Database Articles and Debuted Databases

Although NAR occasionally published small, supplemental issues devoted to compilations of gene sequences throughout the 1980s, articles in a supplemental issue from 1991 began to show a marked movement toward the adoption of database technologies. Both the 1991 and 1992 issues contained many articles that described databases that already existed online or described precursor efforts to develop future web-available databases. The first supplemental issue in 1991 contained just 18 articles and represents a snapshot of change. Several of the databases described were only available via postal delivery of physical media such as floppy disks, CD-ROM, or even paper printout (e.g., see Gupta and Reddy, 1991; Wada et al., 1991; Wells and Brown, 1991). Others offered server access through Gopher or FTP, and yet others indicated an expectation to transition to new forms of access, such as “new-style databases” offered via centralized services (e.g., Giannelli et al., 1991). The first databases accessible via the World Wide Web appeared in 1994, and adoption quickly spread. All told, 53 databases published in NAR Databases Issues started either prior to or concurrent with the advent of the World Wide Web. Of these 53 databases, 40 (75%) transitioned to a web-accessible format and 18 (45%) of those remain available as of December 2016 (Imker, 2018). The last publication that did not offer web access, for the WT1 Gene Database, occurred in 1998 (Jeanpierre et al., 1998). The fact that entire issues, which had grown 5-fold to over 100 articles by 1999, could exclusively cover web-accessible databases is a remarkable change in just 5 years.

By 1999 such a large number of databases had been published that the Database Issue needed a resource itself. First compiled as a list and then moved online a year later, the Molecular Biology Database Collection (MBDC) serves as a continually updated auxiliary resource that facilitates researchers' ability to locate current, relevant databases (Baxevanis, 2000). As submissions to the NAR Database Issue continued to balloon through the early 2000s, a 2009 Database Issue editorial reiterated strict publication criteria that emphasized “thoroughly curated databases that are expected to be of interest to a wide variety of biologists, primarily bench scientists” with a preference toward unique, web-accessible, and persistent databases not described elsewhere (Galperin and Cochrane, 2009). In 2010 it was announced that a new journal, Database: The Journal of Biological Databases and Curation, was launched by Oxford University Press with the hope that this new journal, along with Bioinformatics, would provide an appropriate venue for databases deemed not appropriate for inclusion within an NAR Database Issue. These changes checked growth, and in the following years the issue size leveled out to an average of 183 articles published per year over 2011–2016 (Figure 1).

FIGURE 1

Figure 1. NAR Database Issue growth between 1991 and 2016. Stacked bars represent the total number of articles published in a given year and are further broken down into debut articles (dark gray) and update articles (light gray). Note that here (and figures throughout), the absence of data for 1995 is the result of no publication of a Database Issue that year.

As dissemination of data via the web became the norm, the number of unique databases debuted began to accumulate. Debuts grew steadily throughout the 1990s with a sharp inflection around the turn of the century (Figure 2). The data gathered here show that for over the last decade the rate of database debut has closely followed a linear trend. This rate is necessarily limited due to the capacity of the NAR and the community to handle editorial and peer review responsibilities for associated articles. Despite this constraint, a brisk “proliferation rate” of 104 database debuts/year can be calculated for 2002–2016.

FIGURE 2

Figure 2. The accumulation of unique databases debuted in NAR Database Issues between 1991 and 2016 shows a continual trend of increased growth overall and linear proliferation in recent years, with maximum curvature in 2002.

NAR Database Issues focus on highly-developed, well-curated databases that are expected to be of broad interest to research communities. More databases are developed that are not included within NAR Databases Issues, although a database provider survey conducted for ELIXIR in 2009 indicated that NAR dominates database publication (Southan and Cameron, 2017). Thus, the work here is expected to provide a robust sampling, but also represents a “lower limit” of database proliferation. Especially when considered as a lower limit, this proliferation rate illuminates the magnitude of the sustainability issue. An annual growth rate of 104 new, unique, and high-quality databases that are expected to persist well into the future is not a trivial number. Given erratic funding for scientific research, the ability to indefinitely sustain a growing number of resources—regardless of quality—is a major cause for concern (Merali and Giles, 2005; Baker, 2012). Indeed, multiple successful resources published within NAR Database Issues have suffered. Example databases within this dataset that have amassed >1,500 total citations and yet have moved to variable subscription or donation models include OMIM: Online Mendelian Inheritance in Man, TAIR: The Arabidopsis Information Resource, TRANSFAC, and even KEGG: Kyoto Encyclopedia of Genes and Genomes. As a database with 16,832 total citations, the second highest found in this study, KEGG's value to the community is not ambiguous. Thus, the proliferation rate found for databases published in NAR Database Issues puts pervasive funding issues in starker light as database creators, funders, and researchers grapple with sustainability.

Measures of Database Impact

Databases require financial and organizational support not only for initial creation, but for on-going maintenance and improvement. Most community databases, like other research infrastructure resources, function on unstable grant and institutional support (Kalumbi and Ellis, 1998; Bastow and Leonelli, 2010). This is despite their critical role in modern science; for example, Wren found bioinformatic resources were disproportionately overrepresented among articles with the most citations between 1994 and 2013 (Wren, 2016). Because of the lack of stable support, evidence of impact is highly sought after in order to prioritize allocation of funds and provide compelling justifications for continual government or institutional backing (Mayernik et al., 2017).

Citation analysis has well-acknowledged limitations yet remains a common method to assess impact in many areas of academia. Critics note that citations are not comprehensive indicators of impact since authors rarely cite everything that could or should be attributed (MacRoberts and MacRoberts, 2018). This is true for databases as well, and studies examining full-text journal articles found data citation is often under-reported (Mooney, 2011; Jonkers et al., 2014). Two issues are especially confounding. Without a formal publication, databases in and of themselves generally do not map to a standard bibliographic format required by publishers. Additionally, researchers themselves often do not recognize their data sources as citable. Consequently, references are often relegated to in-line text or not mentioned at all (Mayo et al., 2016). Jonkers et al. found that citations for HAMAP and SWISS- 2DPAGE, two databases included in this work, were underrepresented by 11.1 and 27.8%, respectively, when in-text mentions were evaluated, and results were even more variable when other ExPASY resources where examined (Jonkers et al., 2014).

In light of this underrepresentation, a major push to enable formal citation of data is underway. The Joint Declaration of Data Citation Principles brought together a group of interested parties to develop a set of principles to encourage data citation and acknowledge “data should be considered legitimate, citable products of research” (Martone, 2014). The organization DataCite, founded in 2009, issues digital object identifiers (DOIs) as a way to assign persistent identifiers to datasets (Neumann and Brase, 2014). According to DataCite's statistics dashboard, by the end of 2017 DataCite issued over 12 million DOIs² Additionally, large publishers such as Elsevier now include explicit encouragement in author guidelines to cite the data that support articles. PLOS not only has a strict availability requirement for data that underpins articles, but likewise includes guidance on how to cite databases and repositories. The momentum toward greater acknowledgement of data and data resources is strong, and citation is clearly expected to be an essential metric.

The idea of data citation is certainly not new to database providers, and statements requesting citation can be found within early NAR Database Issue publications (e.g., Jeanpierre et al., 1998). Although the reinvigorated emphasis is welcome, change continues to percolate slowly through academic communities (Mayo et al., 2016). In the meantime, NAR Database Issues publications offer an opportunity to study data citation by proxy and also follow accepted criteria in citation analysis; i.e., citations evaluated in aggregate, and through comparisons within the same year, same type of publication, and from the same discipline (Leydesdorff et al., 2016).

As of December of 2016, citations for all 3,055 Database Issue articles included in this analysis totaled 385,235. New issues show an expected citation lag, but cumulative citations show linear growth with each new issue at a rate of 22,800 citations/issue/year between years 1999 and 2012 (see Supplementary Figure 1).

The results in Figure 3 show database availability correlates to number of citations. These results corroborate a recent analysis of SDARs, which also included databases (Wren et al., 2017). However, this relationship does not hold true for recent articles since citation lag is readily apparent for recent publications. As such, these results indicate that a low number of citations within a few years of debut cannot be used to anticipate that a given database will not be popular. In their survey of >200 database providers, Southan and Camerson found over 90% of providers had <3 years of secured funding, and other surveys have indicated similar instability (Attwood et al., 2015). Since it is likely that additional support will be required during the first few years of a database's debut, early citation metrics should not be relied on as a raw measure of impact. This is an especially important point for those submitting (or evaluating) funding proposals.

FIGURE 3

Figure 3. Total citations (displayed as log10) across all articles that describe a given database are higher for databases that remain available (purple) than for those no longer available (orange). In this analysis of raw citation counts, the time dependence of citations is stronger for currently available databases than those that tested as currently unavailable. Points are scaled to represent the number of databases for a given citation count and debut year. To better fit local means, especially for more recent years, a generalized additive model function was used, and curves are shaded with 95% confidence intervals.

Citations are known to vary wildly, as is true in this analysis. For example, the 2000 article for RCSB PDB: Protein Data Bank is a notable outlier with 16,498 citations. This is 3 times the second most cited article which has 5,558 citations for KEGG: Kyoto Encyclopedia of Genes and Genomes, also published in 2000. To mitigate this issue of skewing and enable more robust comparison across years, article citations were normalized within issues to calculate a Percent Rank which then was averaged across all NAR Database publications for a given database. To account for citation lag only articles and associated databases from 1991 to 2012 are included in this analysis. Percent Rank was then used to bin databases into Citation Quartiles for analysis (Table 1).

TABLE 1

Table 1. Citation analysis for databases with articles published 1991–2012.

The data show a small proportion of databases in the 1st quartile (14%) ultimately represent the majority of the NAR Database Issue citations (72%). For some databases, these citations may accumulate over multiple articles. This percentage jumps to 92% when the top two quartiles, representing 39% of the set, are considered. The percentage of databases still available significantly decreases as the bottom quartile is approached (χ² = 87.508, p < 2.2e-16). In availability testing for this analysis, 80% of the databases in the 1st quartile remain accessible. Considering the apparent impact of databases in this quartile, the absence of 20% is potentially problematic. However, it is possible that these databases migrated in a manner that was not detected in this analysis or have simply outlived their usefulness. An in-depth analysis of these absent, high-ranking databases and the consequence of their absence on the community would aid this assessment.

On the other end of the spectrum, despite the relative lack of citations for databases in the 3rd and 4th quartiles, URL testing showed that 61 and 44%, respectively, persist. Although one might be concerned about the loss of any resource, this may be a natural consequence if low citations indicate the databases were not of as great of utility as originally expected. For example, Galperin suggested some databases may be duplicative or suffer from a narrow scope (Galperin, 2006). In that light, the retention of over 400 databases within these two quartiles is noteworthy. Closer inspection of these resources will help us understand what drives persistence with little external validation in the form of citation. Indeed, there may be important lessons to learn from these databases to apply more broadly. For example, is persistence achievable because of easy to maintain systems? A personal commitment? A different (and possibly more appropriate) measure of impact? With additional validation and refinement, assignment of citation quartiles provides a framework for targeting subsets of databases to assess in more detail, for example, to assess for support needed to sustain, sunset, or even resurrect resources.

Persistence and Maintenance of Databases

Databases published in NAR Database Issues are expected to persist. If databases are neglected, the NAR Database Issue editorial from 2009 stated “respective senior authors (and in some cases, their host institutions) will be prevented from publishing new papers in the NAR Database Issue” (Galperin and Cochrane, 2009). Indeed, these expectations appear to be effective since the rate at which databases published in NAR Database Issues become defunct is low compared to other measures. For example, a 2014 study found that positive responses to requests for data supporting published articles fell by 17% per year (Vines et al., 2014). In contrast, the availability of the databases tested here decreased by only 3.8% per year from 2001 to 2016 (Figure 4), which corroborates an earlier estimation of < 5% per year for databases included in the MBDC (Fernández-Suárez and Galperin, 2013).

FIGURE 4

Figure 4. In this study, URLs for each unique database were tested to determine the rate of database attrition. Databases show linear attrition for those debuted between 2001 and 2016, with maximum curvature in 2001. For earlier years, although the data here indicate retention is highly variable, database availability ceases to decline steadily.

Interestingly, attrition appears to level off for databases debuted between 1991 and 2001. This finding is notable as it allows us to quantify the number of databases that are, in fact, persistent for a long period of time (e.g., ≥15 years). The current availability of databases that debuted between 1991 and 2001 averages to 39.5% and equates to 105 databases. One interpretation of this result is that the community is able to sustain a net positive growth in the number of databases that can be supported long term. In this scenario, although the community is unable to sustain all debuts such that substantial attrition of new databases does occur, a portion of new databases are retained long-term and the total population grows. However, an alternative interpretation of this result is that leveling off represents the approximate maximum capacity the community can sustain, such that existing “established” databases must cycle off to make room for newer databases to hold the population steady at ~100 databases maintained overall. These interpretations represent two very different scenarios, and it is critical that this trend is watched closely in the coming years. Additionally, examination of the 105 long-lived databases in more detail will provide valuable insights into the characteristics that contribute to longevity.

Whether or not a URL continues to resolve to the database is helpful to evaluate if a database is still accessible. However, to measure on-going maintenance of the databases identified in this study, each available database website was inspected for an “update date.” This date may be associated with any number of improvements, such as the addition of new data, development of new features, creation of tutorials, changes to the interface, server upgrades, etc. For the purposes of this study, any such activity was considered an update and was interpreted as evidence that the database is undergoing active monitoring and care. This evaluation is of interest because it signifies on-going effort and helps to assess the need for committed funding.

The same set of currently available databases with articles published between 1991 and 2012 (n = 816) assigned to citation quartiles was used to analyze maintenance. This subset provided the ability to examine maintenance relative to citation quartiles but also ensured the databases included were old enough that the need for update is likely. The results show that an update date could be located for 434 (53%) of these databases (Table 2). Since the inability to locate an update date could be the result of interface complexity or simply that updates are not publicized, the remaining 47% cannot be interpreted as not updated, simply that update is unknown. However, the higher the citation quartile, the more likely that updates were documented, with dates almost twice as likely to be found for a database within the 1st quartile vs. the 4th quartile (χ² = 46.084, p = 1.133e-11). That the presence of an update date, regardless of what the actual date is, correlates to citation ranking is a surprising result and may hint at underlying issues with the usability of the database websites. Readily providing update information allows users to assess how current the database is and may influence users' interpretation of reliability. Biological databases and other bioinformatics resources frequently suffer from a lack of user-centered design (Pavelin et al., 2012; Helmy et al., 2016), and the trend could be related to this phenomenon.

TABLE 2

Table 2. Updates found for currently available databases.

Although a decreasing trend is also related to citation quartile (χ² = 23.599, p = 1.186e-06), analysis of updates revealed that the majority of databases across all quartiles undergo updating after debut (Table 2). While this is helpful to confirm the databases are not static, the timing of updates is also informative as the recentness of update is indicative of continual maintenance. When analyzed, the most predominate update period across all quartiles was also the most recent and occurred in 2016–2017 (Figure 5). This result was significant for 1st (χ² = 160.67, p < 2.2e-16), 2nd (χ² = 108.73, p < 2.2e-16), and 3rd (χ² = 25.386, p = 4.207e-05) quartiles, but not the 4th (χ² = 5.6389, p = 0.2278).

FIGURE 5

Figure 5. Database websites for currently available databases were checked for evidence of updating. Using the sample of databases binned into Citation Quartiles, databases with updates found were assigned to categories of 2-year increments. Updating is correlated to citation quartile, and the largest portion of databases examined were updated within the 2015–2017 time frame.

Especially bearing in mind this sample of databases was restricted to debut prior to 2013, these results show that for the majority of cases where documentation of updates could be found, databases are quite actively maintained. Recent updating was expected for databases that ranked more highly, but the results for the lower-ranking databases is somewhat more surprising. Although those in the 4th quartile are not statistically likely to be updated in a given time period, that any update examples exist again provides notable outliers. This suggests that while citations are one measure on which to evaluate a database, other factors must be identified and explored to understand what motivates the on-going commitment to these resources.

Conclusions

This work set out to gather a comprehensive census of molecular biology databases published in NAR Database Issues to provide a historical look at database development. The results here show rapid adoption of online molecular biology databases, with accumulation of over 1,700 unique databases during the 25 year period covered. Moreover, new databases published within NAR Database Issues are proliferating at a rate of over 100 per year and have been for well over a decade. This work reveals a tremendous appetite and capacity within the community to develop new resources.

Each of these databases was deemed of sufficient promise to be included in the prestigious NAR Database Issue. As such, each represents a significant amount of effort to bring it into existence. These efforts go well beyond acquiring hardware for computation and storage, or even that for development of the database's core architecture, to also include a myriad of activities around data acquisition and curation, quality control and validation, ingest workflows, interface design, and user support. With this in mind, the extent of proliferation uncovered here signals that the molecular biology and bioinformatics communities are remarkably energetic and willing to shoulder significant obligations in order to provide these resources to the boarder life science communities. Challenges will come later in these databases' lifecycles, but during the initial stages of development it appears that funding, access to technology and skilled staff, and enthusiasm for the promise of such resources are not sufficiently limiting to prevent the extent of growth shown here. However, this study also shows that such consistently rapid growth is coupled with a rate of attrition that indicates the ability to establish new databases well exceeds the ability to sustain those databases. As other domains likewise mature, a similar imbalance can be expected unless growth is more carefully metered or long-term support is more readily available.

As we contend with sustaining over 100 new databases per year, demonstration of impact becomes paramount. It is clear data citation will be a key indicator in this evaluation. In some regards, the results of the citation analyses presented here indicate data citation is a valid metric. This work shows that the more cited a database, the more likely it is to be available and the more likely it is to have been updated recently. However, although this study reveals the expected trends overall, it also reveals numerous outlier populations which, if examined in more detail, have the potential to improve our understanding of the nuances related to database impact and persistence. For example, further analysis will help us understand where—and why—citation fails to anticipate availability, whether it's for populations of high-ranking databases no longer available or populations of low-ranking databases somewhat unexpectedly available. Essential questions readily follow: Are high-ranking databases that go fallow simply recreated in a few years? Are low-ranking databases that persist essential, but in ways that elude reliable citation? A deeper examination of where citation fails to be predictive is critical since considerable emphasis is currently being placed on increasing data citation, and there is a danger that citations will disproportionately dominate arguments to justify either the continuation or discontinuation of support. This study shows that while citations and categorization into citation quartiles are useful as exploratory indicators, they are not conclusive measures of impact in and of themselves. In keeping with the Leiden Manifesto on use of bibliometrics to evaluate research, it is critical that use of metrics must not result in “misplaced concreteness and false precision” (Hicks et al., 2015). Indeed, the outliers mentioned above appear especially well-placed to demonstrate the core of this principle, and the databases within this study are posed to serve as a prime example of the necessity of using metrics as a tool to assist expert qualitative evaluation instead of letting metrics stand as unquestioned judgements.

In addition to reliable methods to evaluate database impact, optimized sustainability requires an understanding of the on-going support needed to ensure databases remain current and relevant to users. When evidence could be found, the analyses here shows that the majority of databases were updated recently. This held true even for many of the databases that mapped to the 3rd or 4th citation quartiles and indicates that most databases still available are also, in fact, actively maintained. As an initial evaluation, the criteria used here to determine what constituted maintenance were broadly defined, and the results suggest that a follow-up chronicling explicit maintenance activities would be both worthwhile and illuminative. In the meantime, this initial result is a highly positive finding for those who depend on reliable access to these resources as well as for those that argue the necessity of on-going, committed support. As funders, researchers, and database providers continue to grapple with sustainability issues, the ability to clearly articulate maintenance needs will become increasingly important. The work here provides the first step in this articulation by gathering preliminary evidence to show how frequently databases are updated in practice.

The updates observed here were reported in highly variable ways on database websites, and this serves as an area for improvement. Standardizing how activities are documented and reported would give anyone, including users, the ability to quickly assess the current status of a database's operation. Some mechanisms, such as database certification, already exist and could facilitate the communication of such information. One such example is the Core Trust Seal³, a recent merger between the Data Seal of Approval and the World Data Systems certifications, which is an inexpensive and light-weight process that is applicable to data resources across many domains. More specialized processes may also be appropriate, especially to capture domain-specific metrics. For example, organizations such as ELIXIR, an intergovernmental effort that coordinates and develops life science resources across Europe, is currently working to identify “Core Data Resources” and has outlined a number of indicators to evaluate these resources (Durinx et al., 2017).

While it's likely that sustainability can be improved overall, we must be able to more nimbly address change given some attrition is unavoidable. A consequence of the discrepancy between capacity to develop databases and the capacity to sustain them, particularly in light of increased public access to data, is the need to better embrace the many transitions a resource may undergo. While a database may not necessarily live in perpetuity as debuted, it need not dissolve into oblivion either. Within the databases studied here, numerous examples of proactive migration, merger, sunsetting, and archiving were witnessed. Indeed, database creation and database attrition seem to go hand-in-hand; an article that regretfully announced a discontinuation appeared in just the second Database Issue (Schmidtke and Cooper, 1992). Yet these transitional activities are not widespread, let alone encouraged. Therefore, it is not surprising that examples of neglect, limbo, or complete abandonment were also found. These situations reveal themselves in many ways, some subtle and others not. For example, at the time of this publication, the website for the structural database SCOPe carries a funding alert indicating reliance on volunteer staff time while new funding is sought, but after repeated attempts it is not clear if those efforts will be successful⁴. As a community with a mature history of database development and use, promoting the legitimacy—and even necessity—of these proactive transitional activities would be a positive step forward. As other domains struggle with similar sustainability issues, a model that is more nuanced than “dead/alive” or “success/failure” would be valuable.

Prior to this work, the full extent of database proliferation was unclear. With over 1,700 molecular biology databases created in 25 years and a current pace of >100 new debuts per year, this work reveals the magnitude of the challenge to sustain such vigorous activity. Sustainability is often discussed in the context of a single or small number of resources, but these results indicate a need to move toward assessing sustainability for the community as a whole. The knowledge gained through the preliminary analyses reported here is a step toward this holistic view, and further analyses on subsets of this census will enable a deeper and more nuanced understanding of the activities that contribute to database proliferation, impact, and maintenance.

Data Availability Statement

The data, scripts, and associated documentation for this article are archived and freely available in the Illinois Data Bank at https://doi.org/10.13012/B2IDB-4311325_V1. The author encourages corrections, reanalysis, reuse, and updating. Materials are also accessible on GitHub at https://github.com/1heidi/nar and may be updated since the time of this publication.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author would like to thank colleagues for helpful feedback on the manuscript, especially the Illinois Statistics Office for their comprehensive methods review, Hoa Luong for careful curation of the dataset, and Colleen Fallaw for feedback on the manuscript and documentation as well as independent verification that the deposited R code executed as expected.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frma.2018.00018/full#supplementary-material

Footnotes

1. ^“Online” used here refers to any networked access to resources on the Internet, including the current World Wide Web but also FTP or even earlier transfer protocols such as Gopher. “Web-available” used here is more specific and refers to availability through World Wide Web, which relies on HTTP, URLs, and browsers to provide access to resources on the Internet.

2. ^https://stats.datacite.org/

3. ^https://www.coretrustseal.org/

4. ^https://web.archive.org/web/20180418012723/http://scop.berkeley.edu/about/ver=2.07

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