Tag: data management

Better data practices advance biodiversity knowledge

A framework to retrieve, refine and align secondary biodiversity data with FAIR standards.

Guest blog post by Nubia Marques et al.

In a world increasingly defined by data-driven decisions, biodiversity research stands to benefit from standardized and accessible data. Despite their importance for research, biodiversity datasets often fail to meet FAIR (Findable, Accessible, Interoperable, Reusable) standards, leading to concerns about data quality, reliability, and accessibility.

How to ensure biodiversity data are FAIR, linked, open and future-proof?

To address this, we propose a framework to retrieve, refine and align secondary biodiversity data with FAIR standards, utilizing the Darwin Core model. We followed four steps:

  1. data localization (systematic review)
  2. quality validation
  3. standardization using the Darwin Core standard
  4. sharing and archive in the appropriate repository.

Our approach integrates data validation and quality control steps to ensure that secondary data sets can be trusted.

Our study in Biodiversity Data Journal focused on ecotonal estuarine ecosystems near the easternmost Amazon, where we recovered data from 46,000 individuals representing 3,871 taxa across eight biotic groups (birds, amphibians, reptiles, mammals, fish, phytoplankton, benthos, and plants) from 1985 to 2022. These data were used to illustrate how our strategy improves validation, making the data more reliable for macroecological modeling and conservation management. As data becomes more standardized, researchers around the world will be better equipped to collaborate, identify trends, protect ecosystems, and advance sustainability efforts.

Relationships between numbers of taxa and occurrences gathered through an extensive review of secondary biodiversity data from the Golfão Maranhense area, in the estuarine regions of eastern Amazonia.

Accessible biodiversity data empowers stakeholders and provides critical insights into ecosystem health and species conservation. However, without standardized formats, this data is often fragmented, incomplete, or difficult to compare. By creating a consistent framework for collecting, storing, and sharing data, we are opening the door to more informed decision-making and innovation in biodiversity conservation.

The key to conserving biodiversity is collaboration and transparency. By prioritizing accessible and standardized data, we ensure that vital information reaches those who need it most – whether it’s for scientific study, habitat management or policymaking.

Let’s continue to make biodiversity data a tool for global change!

Research article:

Marques N, Soares CDdeM, Casali DdeM, Guimarães E, Fava F, Abreu JMdaS, Moras L, Silva LGda, Matias R, Assis RLde, Fraga R, Almeida S, Lopes V, Oliveira V, Missagia R, Carvalho E, Carneiro N, Alves R, Souza-Filho P, Oliveira G, Miranda M, Tavares VdaC (2024) Retrieving biodiversity data from multiple sources: making secondary data standardised and accessible. Biodiversity Data Journal 12: e133775. https://doi.org/10.3897/BDJ.12.e133775

How the names of organisms help to turn ‘small data’ into ‘Big Data’

Innovation in ‘Big Data’ helps address problems that were previously overwhelming. What we know about organisms is in hundreds of millions of pages published over 250 years. New software tools of the Global Names project find scientific names, index digital documents quickly, correcting names and updating them. These advances help “Making small data big” by linking together to content of many research efforts. The study was published in the open access journal Biodiversity Data Journal.

The ‘Big Data’ vision of science is transformed by computing resources to capture, manage, and interrogate the deluge of information coming from new technologies, infrastructural projects to digitise physical resources (such as our literature from the Biodiversity Heritage Library), or digital versions of specimens and records about specimens by museums.

Increased bandwidth has made dialogue among distributed data centres feasible and this is how new insights into biology are arising. In the case of biodiversity sciences, data centres range in size from the large GenBank for molecular records and the Global Biodiversity Information Facility for records of occurrences of species, to a long tail of tens of thousands of smaller datasets and web-sites which carry information compiled by individuals, research projects, funding agencies, local, state, national and international governmental agencies.

The large biological repositories do not yet approach the scale of astronomy and nuclear physics, but the very large number of sources in the long tail of useful resources do present biodiversity informaticians with a major challenge – how to discover, index, organize and interconnect the information contained in a very large number of locations.

In this regard, biology is fortunate that, from the middle of the 18th Century, the community has accepted the use of latin binomials such as Homo sapiens or Ba humbugi for species. All names are listed by taxonomists. Name recognition tools can call on large expert compilations of names (Catalogue of Life, Zoobank, Index Fungorum, Global Names Index) to find matches in sources of digital information. This allows for the rapid indexing of content.

Even when we do not know a name, we can ‘discover’ it because scientific names have certain distinctive characteristics (written in italics, most often two successive words in a latinised form, with the first one – capitalised). These properties allow names not yet present in compilations of names to be discovered in digital data sources.

The idea of a names-based cyberinfrastructure is to use the names to interconnect large and small distributed sites of expert knowledge distributed across the Internet. This is the concept of the described Global Names project which carried out the work described in this paper.

The effectiveness of such an infrastructure is compromised by the changes to names over time because of taxonomic and phylogenetic research. Names are often misspelled, or there might be errors in the way names are presented. Meanwhile, increasing numbers of species have no names, but are distinguished by their molecular characteristics.

In order to assess the challenge that these problems may present to the realization of a names-based cyberinfrastructure, we compared names from GenBank and DRYAD (a digital data repository) with names from Catalogue of Life to assess how well matched they are.

As a result, we found out that fewer than 15% of the names in pair-wise comparisons of these data sources could be matched. However, with a names parser to break the scientific names into all of their component parts, those parts that present the greatest number of problems could be removed to produce a simplified or canonical version of the name. Thanks to such tools, name-matching was improved to almost 85%, and in some cases to 100%.

The study confirms the potential for the use of names to link distributed data and to make small data big. Nonetheless, it is clear that we need to continue to invest more and better names-management software specially designed to address the problems in the biodiversity sciences.

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Original source:

Patterson D, Mozzherin D, Shorthouse D, Thessen A (2016) Challenges with using names to link digital biodiversity information. Biodiversity Data Journal, doi: 10.3897/BDJ.4.e8080.

Additional information:

The study was supported by the National Science Foundation.