scholarly journals DINA Bits - Small Services Growing in the DINA System

2018 ◽  
Vol 2 ◽  
pp. e25579
Author(s):  
Falko Glöckler ◽  
Markus Englund
Keyword(s):  
Data Management ◽  
Management System ◽  
Research Data ◽  
Collection Management ◽  
Related Data ◽  
Collection Management System ◽  
Generic Management ◽  
Collection Data ◽  
Taxonomic Research ◽  
Collection Management Data

The DINA system (“DIgital information system for NAtural history data”, https://dina-project.net) consists of several web-based services that fulfill specific tasks. Most of the existing services are covering single core features in the collection management system and can be used either as integrated components in the DINA environment, or as stand-alone services. In this presentation single services will be highlighted as they represent technically interesting approaches and practical solutions for daily challenges in collection management, data curation and migration workflows. The focus will be on the following topics: (1) a generic reporting and label printing service, (2) practical decisions on taxonomic references in collection data and (3) the generic management and referencing of related research data and metadata: Reporting as presented in this context is defined as an extraction and subsequent compilation of information from the collection management system rather than just summarizing statistics. With this quite broad understanding of the term the DINA Reports & Labels Service (Museum für Naturkunde Berlin 2018) can assist in several different collection workflows such as generating labels, barcodes, specimen lists, vouchers, paper loan forms etc. As it is based on customizable HTML templates, it can be even used for creating customized web forms for any kind of interaction (e.g. annotations). Many collection management systems try to cope with taxonomic issues, because in practice taxonomy is used not only for determinations, but also for organizing the collections and categorizing storage units (e.g. “Coleoptera hall”). Addressing taxonomic challenges in a collection management system can slow down development and add complexity for the users. The DINA system uncouples these issues in a simple taxonomic service for the sole assignment of names to specimens, for example determinations. This draws a clear line between collection management and taxonomic research, of which the latter can be supported in a separate service. While the digitization of collection data and workflows proceeds, linking related data is essential for data management and enrichment. In many institutions research data is disconnected from the collection specimen data because the type and structure cannot be easily included in the collection management databases. With the DINA Generic Data Module (Museum für Naturkunde Berlin 2017) a service exists that allows for attaching any relational data structures to the DINA system. It can also be used as a standalone service that accommodates structured data within a DINA compliant interface for data management.

scholarly journals DINA: Open Source and Open Services - A modern approach for natural history collection management systems and research

2019 ◽  
Vol 3 ◽  
Author(s):  
Falko Glöckler ◽  
James Macklin ◽  
Fredrik Ronquist ◽  
Jana Hoffmann
Keyword(s):  
Natural History ◽  
Open Source ◽  
Management System ◽  
Research Data ◽  
Collection Management ◽  
Modern Approach ◽  
Natural History Collection ◽  
Collection Management System ◽  
Model Complex ◽  
Code Development

The DINA Consortium (“DIgital information system for NAtural history data”, https://dina-project.net ) was formed in order to provide a framework for like-minded large natural history collection-holding institutions to collaborate through a distributed Open Source development model to produce a flexible and sustainable collection management system. Target collections include zoological, botanical, mycological, geological and paleontological collections, living collections, biodiversity inventories, observation records, and molecular data. DINA is funded by the participating member institutions. DINA Core Members are organizations or individuals who commit at least one half-time equivalent of resources to the development of the consortium goals, at least half of which should be available for code development. The DINA system is architected as a loosely-coupled set of several web-based modules. The conceptual basis for this modular ecosystem is a compilation of comprehensive guidelines for Web application programming interfaces (APIs) to guarantee the interoperability of its components. Thus, all DINA components can be modified or even replaced by other components without crashing the rest of the system as long as they are DINA compliant. Furthermore, the modularity enables the institutions to host only the components they need. DINA focuses on an Open Source software philosophy and on community-driven open development, so the contributors share their development resources and expertise outside of their own institutions. One of the overarching reasons to develop a new collection management system is the need to better model complex relationships between collection objects (typically specimens), research data and associated workflows. We will present the enhancements provided by the approach of the DINA system focussing on the flexibility to plug in compliant components and accommodate additional (meta-)data and specimen related research data with the help of a generic data module. Furthermore, we will discuss challenges in the governance of the development activities such as organizing the distributed code development of the core modules, the code review process and the choice of the software stack. These organizational challenges will be overcome with the help of a revised Memorandum of Understanding.

scholarly journals From Field to Web: Pre-entry point digitisation

2019 ◽  
Vol 3 ◽  
Author(s):  
Vladimir Blagoderov
Keyword(s):  
Mobile Phones ◽  
Management System ◽  
Additional Data ◽  
Imperial College ◽  
Collection Management ◽  
Event Data ◽  
Collection Management System ◽  
Collection Data ◽  
Android App ◽  
The Moment

Most digitisation workflows are focused on legacy material, due to the sheer number of objects already collected. However, it is just as important to develop protocols for digitisation of incoming material to reduce accumulation of an additional backlog. This is especially crucial with the advent of molecular collections and field sequencing. In-the-field extraction and sequencing (Oxford Nanopore Technologies 2018) may lead to increasing numbers of voucher specimens without proper collection data and labels; or specimens disassociated with data. It is easy for researchers occupied by collecting and sequencing to delay proper documentation until a later date. As a curator, I can vouch that specimens without properly recorded data (with only collecting codes, for example) are lost for science. Fortunately, a combination of the best collecting and curatorial practices, simple online and offline tools, and modern technologies, makes in-the-field digitisation a reality. In the last couple of years, entomologists at the National Museums Scotland (NMS) have been testing the following workflow: Collecting routes and points are recorded with ViewRanger (Augmentra Ltd 2019), available as an app for mobile phones; At the moment of collecting, event data is recorded with Epicollect5 (Imperial College London 2019), available as Android app. Software's field generator allows creation of different scenarios, depending on method or circumstances of collection; and records main types of data: text, dates, time, coordinates. Individual collecting code is associated with the record; Specimens collected are prepared (pinned, stored in preservative, dried, etc.) and associated with corresponding collecting code; Additional data (diary records) is recorded in a notebook with Neo Smartpen (NEO SMARTPEN Inc. 2017) and digitsed. Collecting event records are imported into a collection management system (CMS) (PAPIS, Pape and Ioannou 2019) or EarthCape (EarthCape 2019); Specimen lots (if relevant) are sorted to a desirable level; Multiple specimen or lot records are created in CMS based on collecting event records; Data labels and UID labels are printed and physically associated with specimens or lots; Additional data (klm file of collecting route, diary records) are imported and associated with collecting events. Collecting routes and points are recorded with ViewRanger (Augmentra Ltd 2019), available as an app for mobile phones; At the moment of collecting, event data is recorded with Epicollect5 (Imperial College London 2019), available as Android app. Software's field generator allows creation of different scenarios, depending on method or circumstances of collection; and records main types of data: text, dates, time, coordinates. Individual collecting code is associated with the record; Specimens collected are prepared (pinned, stored in preservative, dried, etc.) and associated with corresponding collecting code; Additional data (diary records) is recorded in a notebook with Neo Smartpen (NEO SMARTPEN Inc. 2017) and digitsed. Collecting event records are imported into a collection management system (CMS) (PAPIS, Pape and Ioannou 2019) or EarthCape (EarthCape 2019); Specimen lots (if relevant) are sorted to a desirable level; Multiple specimen or lot records are created in CMS based on collecting event records; Data labels and UID labels are printed and physically associated with specimens or lots; Additional data (klm file of collecting route, diary records) are imported and associated with collecting events. Steps 1-4, and, depending on available facilities, steps 5-9, can be performed in the field, before specimens reach the depository. Alternatively, steps 5-9 should be performed immediately on returning from the field. There is no excuse for newly collected material not to be digitised before it is reaches the collection. Recent entomological collecting trips of NMS yielded 7358 specimens from 72 collecting events, fully documented and digitised in a matter of hours.

scholarly journals BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data

2021 ◽  
Vol 9 ◽  
Author(s):  
Javad Chamanara ◽  
Jitendra Gaikwad ◽  
Roman Gerlach ◽  
Alsayed Algergawy ◽  
Andreas Ostrowski ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Research Data ◽  
Environmental Data ◽  
The Self ◽  
Data Management System ◽  
Maturity Model ◽  
Ecological Data ◽  
Self Assessment ◽  
Research Data Management

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research. We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel. During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved. This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.

scholarly journals What to do with audiovisual carriers after their digitization?

2017 ◽  
Author(s):  
Brecht Declercq ◽  
Loes Nijsmans
Keyword(s):  
Decision Making ◽  
Life Span ◽  
Management System ◽  
Best Interests ◽  
Collection Management ◽  
Collection Management System ◽  
Digital Collection ◽  
Preservation Conditions ◽  
Decision Making Framework ◽  
Value Preservation

Both traditional and more recent audiovisual carriers degrade. Even CD-ROMs have typically only a ten-year expected life span. In addition, playback equipment for both analogue and digital carriers will ultimately grow scarcer and more expensive to repair or replace. Archives and museums are inevitably faced with the decision of whether to preserve audiovisual carriers after their content has been digitized. This paper o ers a draft decision- making framework developed by the Flemish Institute of Archiving (VIAA). Assuming that an institution already has a digital collection management system in place, the proposed framework addresses the concepts of favourability, possibility, value, preservation conditions and the risk for other carriers through a series of questions. The paper also addresses the disposal of carriers, should an organization decide that disposal is in the best interests of its collections.

scholarly journals Repatriation of Augmented Information to an Institutional Database

2018 ◽  
Vol 2 ◽  
pp. e26479
Author(s):  
Sharon Grant ◽  
Janeen Jones ◽  
Kate Webbink ◽  
Rob Zschernitz
Keyword(s):  
Information System ◽  
Data Quality ◽  
Management System ◽  
Real Life ◽  
Lessons Learned ◽  
Collection Management ◽  
Network Information ◽  
Field Museum ◽  
Collection Management System ◽  
Data Pipeline

On the 9th of April 2010 the Field Museum received a momentous email from the ORNIS (ORnithology Network Information System) team informing them that they could now access the products of a nationwide georeferencing project; its bird collection could be, quite literally, put on the map. On the 7th of August 2017 those data (along with the sister datasets from FISHNet (FISH NETwork) and MaNIS (Mammal Network Information System) finally made their way into the Museum’s collection management system. It's easy to get data out, why is it so hard to get it back? To make it easier, what do we need to do in terms of coordination, staffing, and/or technological resources? How can tools like data quality flags better accommodate the needs of data-providers as well as data-users elsewhere along the collections data pipeline? We present a real life case studyof repatriating an enhanced dataset to its institute of origin, including details on timelines, estimates of effort, and lessons learned. The best laid repatriation protocols might not prepare us for everything, but following them more closely might save us some sanity.

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