JournalTOCs

Biodiversity Information Science and Standards
Number of Followers: 5

This is an Open Access Journal

Open Access journal
ISSN (Online) 2535-0897
Published by Pensoft

[58 journals]

Digitization Planning for Large Collections and Agencies with Specify 7
- Abstract: Biodiversity Information Science and Standards 8: e133987
  DOI : 10.3897/biss.8.133987
  Authors : Grant Fitzsimmons, Theresa Miller : The Specify Collections Consortium (SCC) supports research collections around the world with an intuitive, robust, and highly-customizable software platform to digitize their holdings and manage their collections. For over 25 years, Specify has provided and sustained biological research museums and biorepositories with software for managing, integrating, and publishing collections information (Fig. 1).The SCC has increasingly engaged with very large collections managed by institutions ranging from universities to individual government departments to entire federal agencies, to envision a framework that supports standard computerization workflows and best practices. From our involvement in working with these institutions, which have successfully navigated the transition to Specify, we have gathered insights and learned lessons that may be helpful to others to better anticipate and address key data-related issues in their own institution when embarking on a Specify (or any collections management system) implementation.Understanding how to approach data preparation for import into a collections management system is crucial, as collections often face significant challenges in this area. We will discuss some of the key hurdles that collections encounter and methods to overcome them such as unstandardized data within a single collection and between multiple collections, unparsed names for taxonomy and people, and reconciling pre-existing custom data structures with the fixed structure of a collections management system.We will also cover the decision-making process for determining what collection data should be shared between collections versus what should be managed separately (scoping, data sharing, and data exporting). Large institutions often have multiple collections with diverse needs, and finding the right balance between centralized coordination and individual collection autonomy can be complex. The presentation will discuss the factors considered and the strategies employed to navigate this balance, as it can have significant implications for the overall success of a Specify deployment (Fig. 2).In addition to the technical considerations, the presentation will also delve into the human side of transitioning to a new collections management system. Successful collections management platform implementations require not only technical expertise, but also effective communication and collaboration among the collections staff, volunteers, and management. This presentation offers a framework to help other large collections successfully transition to Specify. Our team at the Specify Collections Consortium has extensive experience in supporting the transition of large research collections to their robust and highly customizable software platform. We have gained valuable insights into the key challenges and best practices for data preparation, system implementation, and change management. By sharing the lessons learned from these institutions and the SCC's experience, the presentation provides valuable insights and guidance to support other organizations in their collections management system implementation projects. A better understanding of this process will ultimately contribute to preserving and improving access to natural history collections (and soon geoscience collections) worldwide. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
Who’s Flying the Plane' Navigating Multi-Stakeholder CMS
Development Demands
- Abstract: Biodiversity Information Science and Standards 8: e133506
  DOI : 10.3897/biss.8.133506
  Authors : Erica Krimmel, Emily Braker, Andrew Doll, Teresa Mayfield-Meyer, Elizabeth Wommack : Collection management systems (CMS) were once digital reproductions of paper catalogs, but now they are expected to provide much more than a catalog number and basic collection information. In recent decades, systems developed over centuries to manage physical specimens have evolved into unwieldy digital storehouses. Complexity in a CMS is introduced by stakeholders at all angles: collections staff, researchers, institutions, metadata standards, national and international priorities, digitization efforts, and technologists. Here we explore problems and opportunities through the lens of Arctos, which has been part of this digital evolution as both a CMS product and a user community for nearly 30 years (Cicero et al. 2024).In 2024, Arctos membership is international and includes 57 institutions with 330 collections managed by 270 active database users how can it do so in sync with the broader community'Cicero et al. 2024). Complexity—even when only considering Arctos’ internal stakeholders—is inherent to the data model. Agents, or the people and organizations who interact with collection objects, provide a useful case study. As a nascent digital system in the 1990s and early 2000s, Arctos’ model was groundbreaking in its decision to treat agents as entities with their own metadata that could be shared across collection objects. Paper catalogs did no such thing. In the past few years, the Arctos community has seen value in fully utilizing and even expanding Arctos’ ability to capture and connect primary source and biographical information about agents. The biodiversity collections community is building momentum behind sharing people data in more and better ways (Groom et al. 2022, Weeks et al. 2024), and the broader museum and academic communities are evaluating how our data need to change in order to be equitable and inclusive (e.g., by developing approaches for handling diacritics). As Arctos continues to respond to boundary-pushing development opportunities,At present, the Arctos community is continually balancing needs between diverse internal stakeholders. Conflicting interests are not necessarily bad; they exist because Arctos is capable of managing complex information, and the Arctos community recognizes that consensus-building presents challenges (Cicero et al. 2024). Turning tensions into development productivity requires a framework for decision-making, and processes that facilitate the alignment of expectations. As much as Arctos has been developing data infrastructure over the past two decades, it has also been developing community processes. Arctos committees, working groups, subcommittees, and staff all meet regularly and follow well-intentioned social norms. There is a GitHub space to organize work and have discussions. A handbook documents shared knowledge. Yet despite such good and continual progress, in today’s under-resourced landscape it still feels like the Arctos community is building the plane while flying it, with a route that changes based on the prevailing winds.This is in part because CMS teams are often skeleton crews. Arctos has only two full-time staff (a lead programmer and a community coordinator), plus one part-time database administrator. In addition to increasing overall staffing, roles that explicitly support structuring and maintaining systems to help developers understand and balance the expectations of users are required. Such roles may be described as “product manager,” “project manager,” “user experience manager,” “business analyst,” etc. At the intersection of CMSs, institutions, and bio-geodiversity communities, one might hope to find paid staff in such roles, driving globally-coordinated development. T he global bio-geodiversity data community needs people whose time and expertise are dedicated to bridging physical collections with digital information systems, connecting experts from one field with experts in another, and developing basic infrastructure that can benefit a diversity of users.What could be accomplished if there were more people working at this intersection, and working from a human-centered design perspective (e.g., see examples from 18F, the United States’ digital.gov, the United Kingdom’s Central Digital and Data Office)' The shared needs of collections staff could be translated into global development priorities, e.g., by canvassing the community at large to define stakeholder requirements for a given request to add or modify a term in one of the community standards. Development teams, including those within and external to CMS domains, could have time to collaborate, identify opportunities and reuse applicable resources. For example, the Database of Global Administrative Areas (GADM) is a resource that Arctos leverages for decision-making and data quality related to geography. Arctos is designed to benefit from the expertise of external authorities, but cannot do this well without people-power dedicated to reviewing and developing a solid understanding of each resource, and to collaborating with external resource developers.Technical progress cannot hope to succeed without centering the people involved. From inception, the idea of a CMS was centered on the individual users and local institutions who provide day-to-day care of natural science collections. Here, we explore methods for prioritizing the human side of CMS development such that it maintains that core purpose, while also connecting to the extended vision of national and international collaborative networks, data flows and decision processes. HTML
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
DiSSCover the Potential of FAIR Digital Object Annotations and How You Can
Use Them!
- Abstract: Biodiversity Information Science and Standards 8: e133172
  DOI : 10.3897/biss.8.133172
  Authors : Tom Dijkema, Sam Leeflang : The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year’s session, we would like to focus on the human-facing application: DiSSCover.DiSSCover is the graphical user interface through which users can interact with Findable, Accessible, Interoperable and Reusable (FAIR) Digital Objects (FDOs), facilitating the curation and enhancement of specimen data (Islam 2024). Development started in 2022 and is ongoing. The interface acts as a gateway into the DiSSCo infrastructure, providing access to digital specimens and media. Extracted from the core DiSSCo API, the data is converted into an easily readable format and made discoverable through a diverse set of filters. DiSSCover’s main focus is to allow users to make annotations upon the data.Through the concept of annotations, we connect expert and machine-generated information, to create extended digital specimens (Hardisty et al. 2022), e.g, by creating linkages to other infrastructures, correcting or adding new information, or by triggering machine annotation services. Machine annotation services are automated scalable tools that run in the background and automatically curate and extend the specimen (Addink et al. 2023). Human users will remain important, as all annotations made by machine annotation services can be reviewed by a trusted person. Annotations are Fair Digital Objects and target a specific part of a specimen, be it a data fragment or an associated media file.At the heart of DiSSCover lies the Open Digital Specimen data specification (Leeflang and Addink 2023). It tries to harmonise multiple data standards into one generic specification based on the new Global Biodiversity Information Facility (GBIF) Unified Model (Robertson et al. 2022). The data is stored as JavaScript Object Notation (JSON) based on JSON Schemas (Anonymous 2024). Annotations are linked to specific data attributes using a JSON-path as the identifier. Data attributes can be individual terms, collections of terms called classes, or the whole object. This creates a flexible but complex data structure, the basis for which we used the World Wide Web Consortium (W3C) web annotation data model (Sanderson et al. 2017).The W3C Web annotation data model contains two main components: the target and body. The target specifies which data attribute the annotation is made on, for example, the term: ‘ods:specimenName’. This is a local term within the open Digital Specimen namespace (ods), which holds the accepted name of the digital specimen. The annotation body holds the value(s) that are appended to the digital specimen, and differ based upon the annotation motivation. DiSSCo recognises five different annotation motivations: addition, modification, comment, assessment and deletion, each of which has its own unique function. This creates a flexible structure that should be able to handle any information the user wants to add to the object. The challenge of DiSSCover is to preserve the complex structure of annotations, whilst making it convenient for users to work with.The session will provide a look at the different kinds of annotations and their use from a practical perspective. A demonstration of DiSSCover will show how users can create annotations, providing knowledge about the process that will give shape to DiSSCo’s main goal of enriching natural history data. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
Bridging Data Standards and FAIR Principles in Biodiversity Digital
Twinning: Prototyping, Challenges, Lessons Learned, and Future Plans
- Abstract: Biodiversity Information Science and Standards 8: e133089
  DOI : 10.3897/biss.8.133089
  Authors : Sharif Islam, Julian Lopez Gordillo, Dag Endresen, Carrie Andrew : Digital twins combine modelling, domain knowledge, computing power, and multiple datasets to offer the potential to unlock new insights into biodiversity (de Koning et al. 2023). The Biodiversity Digital Twin (BioDT) project pioneers this approach to aid in understanding biodiversity through prototyping digital twins (Golivets et al. 2024). However, working with biodiversity data presents challenges due to their dynamic and diverse nature as well as the need for having to deal with incompleteness, uncertainties (Rocchini et al. 2011), disproportionate representation patterns in global studies from wealthier economies (Hughes et al. 2024), and issues with data aggregation and integration (Wüest et al. 2020). Similar to BioDT, there are also plans for creating a Digital Twin of the Ocean (DTO). DTO-Bioflow project addresses these data challenges in the marine domain, where studies show that although European seas host 48,000 marine species (75% described), the data are not yet FAIR (Findable, Accessible, Interoperable, and Reusable) (Ramírez et al. 2022). These challenges hamper the modelling capabilities needed for effective predictions and conservation prioritisation.The adaptability of digital twins across temporal and spatial scales and their ability to model dynamic ecosystems make them ideal for biodiversity research and real-time conservation efforts. However, their success hinges on the consistent integration and alignment of data from disparate sources (Trantas et al. 2023). This integration involves standardising terms used to describe datasets, such as temporal coverage or controlled vocabularies like "Forest" for targetHabitatScope. Thus, adopting data standards is essential. Additionally, challenges such as model bias (Lewers 2023), research context, and data provenance must be considered, adding complexity to metadata capture and alignment.BioDT addresses these challenges with modular building blocks for data integration, model deployment, and workflow management. This approach facilitates the gradual adoption of data standards and FAIR principles, which need to encompass not just data, but also models and software. As automation, ease of reproducibility, and deployability are critical for digital twinning success, data integration and interoperability issues may arise due to missing or insufficient parameter descriptions in the model, incomplete information on data selection, and the unavailability of required software package details. Thus, data standardisation provides a pathway for a consistent approach that can be adopted for different use cases.Common data sources in BioDT, like species occurrences and environmental variables, benefit from standards such as Darwin Core (Wieczorek et al. 2012) and the Ecological Metadata Language (Jones et al. 2019). While valuable, these standards may not fully encompass the complexity needed for comprehensive biodiversity digital twins. Additionally, differing familiarity with these standards and FAIR principles among communities pose challenges. Continuous adoption of data standards, alongside exploring complementary approaches like schema.org or bioschemas.org for capturing diverse (meta)data, is essential. Collaboration with data providers, modellers, and various research infrastructures is also crucial (Andrew et al. 2024).We share our experience using Research Object Crate (RO-Crate), leveraging common JavaScript Object Notation for Linked Data (JSON-LD) representation for metadata profiles and workflow representation, to connect with different infrastructures. In the BioDT project, we are working with various use cases to create prototype digital twins that can serve as valuable resources for other projects. The evolving landscape of digital twin concepts, along with other European Union-funded initiatives like DTO-Bioflow and Destination Earth (DestinE), emphasises the importance of alignment within the digital twin ecosystem. BioDT is committed to aligning with and contributing to this broader context, highlighting the critical role of data standardisation and FAIR implementation. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
Revolutionizing Plant Pathogen Conservation: The Past, Present, and Future
of AI in Preserving Natural Ecosystems
- Abstract: Biodiversity Information Science and Standards 8: e133055
  DOI : 10.3897/biss.8.133055
  Authors : Sue Han Lee, Zhe Rui Liaw, Yu Hao Chai, Shien Lin Ng, Pierre Bonnet, Hervé Goëau, Alexis Joly : Traditionally, plant pathologists have emphasized controlling crop pathogens, neglecting the importance of conserving their diversity in natural ecosystems. Native plant pathogens thriving in natural environments significantly contribute to ecosystem structure, stability, nutrient cycling, and productivity. The coevolution of wild crop progenitors with native pathogens yields a diverse array of disease resistance factors, serving as a critical resource for farmers and breeders in developing disease-resistant cultivars. Moreover, native plant pathogens hold promise as valuable research tools, model systems for scientists, and potential sources of novel drugs, pesticides, bio-control agents, and biotechnological innovations (Ingram 1999, Ingram 2022)Artificial Intelligence (AI) technology, specifically Deep Learning (DL), is revolutionizing agricultural sustainability by advancing plant disease identification (Ayoub Shaikh et al. 2022, Wongchai et al. 2022). DL extends beyond single-crop disease identification, encompassing multiple crops and diseases (Lee et al. 2020). Utilizing computer vision and Internet of Things (IoT), AI enhances the ability to recognize and categorize plant diseases across diverse agricultural landscapes (Sinha and Dhanalakshmi 2022). The data used is crowdsourced from images captured through cameras or smartphones.However, to comprehensively monitor and understand plant diseases on a larger scale for diversity study, AI practitioners should integrate broader factors into their predictive modeling such as weather patterns, geographical variations, environmental conditions, and real-world challenges. Overcoming real-world challenges involves addressing previously unseen diseases, modeling disease distribution across extensive geographical areas, and managing domain adaptation, which arise from differences in data distributions between the source and target domains.An innovative plant disease identification framework has been established, benchmarked on the largest plant disease dataset (Mohanty et al. 2016). This framework specifically focuses on addressing the crucial challenges in this field. The reliability of DL models was thoroughly analyzed and evaluated through machine vision interpretation, and cases lacking labeled data were explored (Chai et al. 2023). This presentation will not only highlight the significant advancements achieved but also outline future plans to new ecological studies of plant diseases identification as indispensable elements in the broader landscape of global environmental change research. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
Initiatives to the Global Representation of Scientific Collections
- Abstract: Biodiversity Information Science and Standards 8: e133022
  DOI : 10.3897/biss.8.133022
  Authors : Lily Shrestha, Chihjen Ko, Laban Musinguzi, Leonardo Buitrago, Melissa Jean-Yi Liu, Oleg Borodin, Salza Palpurina, Tsiky Rabetrano, Vijay Barve, Marie Grosjean : Ensuring that all global scientific collections become accessible online is a challenge. The Global Biodiversity Information Facility (GBIF) maintains a Global Registry of Scientific Collections (GRSciColl) but as of May 2023, 58% of the collections included in the registry were from institutions based in Europe or North America. This shows a low representation of organizations from other regions. GBIF is addressing the bias with the help of its participant network and regional support teams. The GBIF participant network is actively encouraging institutions in its regions to register and describe their collection data through GBIF and GRSciColl. To complement this effort, the regional support teams based in Africa, Asia, Latin America and the Caribbean, and Europe are engaged in mobilizing new collections and updating existing ones (Fig. 1). In addition, a community of editors review changes proposed via a user suggestion system and keeps the entries in GRSciColl updated.The work of the regional support teams involves reviewing institutional web pages or other available online sources to update GRSciColl entries. In cases where the data are not accessible on the web, contacts from the institutions are invited to suggest changes to their GRSciColl pages. As a next step, institutions are contacted to inquire about their interest and plan for sharing open access data. If the response is positive, technical support is offered to help the institutions mobilize and publish the collection data through GBIF.The regional support teams have contacted a number of institutions and several institutions have shown interest in publishing collections on GBIF. Some organizations have already started digitizing and publishing their collection(s). With the training and technical support from the regional support teams, the following institutions have started to share data on GBIF:Instituto de Biodiversidad de Venezuela - INBIO has published data on 23,414 herbarium specimens. Natural History Museum, Tribhuvan University, Nepal has started digitizing its collections and has published eight datasets constituting 1,776 specimens. National Museum of Natural History at the Bulgarian Academy of Sciences has started digitizing 31 out of its 32 collections, of which they have published two herbarium collections represented as two datasets consisting of over 6,357 specimens.This presentation will highlight the data mobilization activities of the institutions, the progress of the GBIF regional support teams (Fig. 2), and the opportunities and challenges experienced in the mobilization effort. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
What Is Sustainability' A Reflection From the Perspective of
Biodiversity Informatics
- Abstract: Biodiversity Information Science and Standards 8: e132930
  DOI : 10.3897/biss.8.132930
  Authors : Giuditta Parolini, Mareike Petersen : In recent years, the word sustainability has been used in multiple domains ranging from finance to packaging, and the list of products and activities claiming to be sustainable grows day after day. However, a general and unambiguous definition of the term sustainability is not available and might not even be advisable because sustainability is a multifaceted concept that takes its meaning from the context of use and cannot provide a set of rules applicable everywhere (Ramsey 2015). Therefore, it remains a task for all the fields that strive to be sustainable to carve out a working definition that suits their needs.Addressing the meaning of sustainability in biodiversity informatics requires paying special attention to the data practices and infrastructures of this discipline. Over time, there has been a steep increase in the amount of biodiversity data created and shared and, at the same time, a rapid proliferation of use cases for biodiversity data. For instance, fields as different as spacial planning (Underwood et al. 2018), climate change research (Hirsch 2017), and finance (Elliot et al. 2024) rely now on biodiversity data. This growth can become unmanageable for biodiversity science, if its data practices and infrastructures do not scale up efficiently and are not sufficiently flexible to cater to the needs of a larger and more varied group of stakeholders.Biodiversity informatics has mainly addressed the financial sustainability of its infrastructures so far (Cook and Cochrane 2023; Kalfatovic et al. 2023). However, environmental and social sustainability are equally important to ensure that biodiversity science achieves its full potential. For instance, data infrastructures with lower environmental impact typically require less electricity to run continuously, and this will save money in the long run, especially at times of significant fluctuations in energy prices. Again, if biodiversity data are collected and shared while keeping in mind the necessities of multiple stakeholders, the user community will become larger and this, in turn, will offer the chance to develop fairer cost contribution agreements for running data infrastructures. More examples could be added to make the case that the three pillars of sustainability—economy, society, environment (Purvis et al. 2018)—are all equally relevant when examining data practices and infrastructures in biodiversity science.Hence, there is a need to open up a discussion on sustainability within the biodiversity community, and to promote an exchange on the topic among different stakeholders interested in biodiversity data and working with them. A community effort is required to agree upon an effective definition of sustainability in biodiversity informatics. Only such a community effort can translate this definition into practical criteria and recommendations for the sustainability of biodiversity data and infrastructures, and set milestones for their implementation.The Society for the Preservation of Natural History Collections and Biodiversity Information Standards (TDWG) are authoritative members of the biodiversity community. The aim of the talk is to involve their members in the discussion on data practices and infrastructures, addressing all the three pillars of sustainability: economy, society, and environment. HTML XML PDF
  PubDate: Wed, 7 Aug 2024 12:06:19 +0300
Meeting Report for the Phenoscape TraitFest 2023 with Comments on
Organising Interdisciplinary Meetings
- Abstract: Biodiversity Information Science and Standards 8: e115232
  DOI : 10.3897/biss.8.115232
  Authors : Jennifer C. Girón Duque, Meghan Balk, Wasila Dahdul, Hilmar Lapp, István Mikó, Elie Alhajjar, Brenen Wynd, Sergei Tarasov, Christopher Lawrence, Basanta Khakurel, Arthur Porto, Lin Yan, Isadora E Fluck, Diego Porto, Joseph Keating, Israel Borokini, Katja Seltmann, Giulio Montanaro, Paula Mabee : The Phenoscape project has developed ontology-based tools and a knowledge base that enables the integration and discovery of phenotypes across species from the scientific literature. The Phenoscape TraitFest 2023 event aimed to promote innovative applications that adopt the capabilities supported by the data in the Phenoscape Knowledgebase and its corresponding semantics-enabled tools, algorithms and infrastructure. The event brought together 26 participants, including domain experts in biodiversity informatics, taxonomy and phylogenetics and software developers from various life-sciences programming toolkits and phylogenetic software projects, for an intense four-day collaborative software coding event. The event was designed as a hands-on workshop, based on the Open Space Technology methodology, in which participants self-organise into subgroups to collaboratively plan and work on their shared research interests. We describe how the workshop was organised, the projects developed and outcomes resulting from the workshop, as well as the challenges in bringing together a diverse group of participants to engage productively in a collaborative environment. HTML XML PDF
  PubDate: Wed, 6 Mar 2024 09:30:43 +0200

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