Research data management in ecology: a practical approach for long-term projects

2002 ◽  
Author(s):  
J.W. Brunt
Keyword(s):  
Data Management ◽  
Research Data ◽  
Practical Approach ◽  
Research Data Management

scholarly journals How long can we build it? Ensuring usability of a scientific code base

2021 ◽  
Vol 16 (1) ◽  
pp. 11
Author(s):  
Klaus Rechert ◽  
Jurek Oberhauser ◽  
Rafael Gieschke
Keyword(s):  
Data Management ◽  
Source Code ◽  
Research Data ◽  
Scientific Contribution ◽  
Scientific Publications ◽  
Research Data Management ◽  
Code Base

Software and in particular source code became an important component of scientific publications and henceforth is now subject of research data management.  Maintaining source code such that it remains a usable and a valuable scientific contribution is and remains a huge task. Not all code contributions can be actively maintained forever. Eventually, there will be a significant backlog of legacy source-code. In this article we analyse the requirements for applying the concept of long-term reusability to source code. We use simple case study to identify gaps and provide a technical infrastructure based on emulator to support automated builds of historic software in form of source code.  

scholarly journals First InvenioRDM Long-Term Support (LTS) version released today ??? and Front Matter is joining as a participating partner

2021 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Data Management ◽  
Open Source ◽  
Research Data ◽  
Management Platform ◽  
Research Data Management ◽  
Data Management Platform

The open source research data management platform InvenioRDM today announced the first Long-Term Support (LTS) release, usable on production services. And I am joining the effort as a participating partner via Front Matter, ...

scholarly journals Perception of Researchers & Academicians of Parul University towards Research Data Management System & Role of Library

2020 ◽  
Vol 40 (03) ◽  
pp. 139-146 ◽  
Author(s):  
Anjana R Bunkar ◽  
Dhaval D. Bhatt
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Developed Countries ◽  
Research Data ◽  
Data Management System ◽  
The Public ◽  
Research Data Management ◽  
Initial Stage

Research data management is a system that helps in archiving and retrieving of research data to reuse and preserving them for long term use. Many universities in developed countries have already started providing RDM services to their researchers and academicians. In India, it is still in the initial stage. The purpose of the present study is to investigate the perceptions of researchers and academicians of Parul University on research data management and research data sharing. It also explores the ways the researchers preserved their research data for future use. It also explores the ways the library can take initiatives to encourage and extend support to the researchers and academicians to the organisation, preservation, and sharing of research data. To investigate and study the problem 100 questionnaires were distributed. There are 88 responses we received out of 100. The study revealed that the majority of respondents were agreeing about the research data sharing and free accessibility of research data to browse and reuse. Researchers are very much interested and agreed in the library’s involvement in organizing and preservation of research data. Researchers and faculty members are more concerned about their intellectual property rights while sharing the data on the public domain.

The Minimum Mandatory Metadata Sets for the KIM Project and RAIDmap

2017 ◽  
pp. 37-64
Author(s):  
Alexander Ball ◽  
Mansur Darlington ◽  
Christopher McMahon
Keyword(s):  
Data Management ◽  
Information Management ◽  
Mechanical Engineering ◽  
Research Data ◽  
Minimal Set ◽  
Life Project ◽  
Research Data Management ◽  
Metadata Record

A Minimum Mandatory Metadata Set (M3S) was devised for the KIM (Knowledge and Information Management Through Life) Project to address two challenges. The first was to ensure the project's documents were sufficiently self-documented to allow them to be preserved in the long term. The second was to trial the M3S and supporting templates and tools as a possible approach that might be used by the aerospace, defence and construction industries. A different M3S was devised along similar principles by a later project called REDm-MED (Research Data Management for Mechanical Engineering Departments). The aim this time was to help specify a tool for documenting research data records and the associations between them, in support of both preservation and discovery. In both cases the emphasis was on collecting a minimal set of metadata at the time of object creation, on the understanding that later processes would be able to expand the set into a full metadata record.

Managing collaborative research data for integrated, interdisciplinary environmental research

2020 ◽  
Author(s):  
Michael Finkel ◽  
Albrecht Baur ◽  
Tobias K.D. Weber ◽  
Karsten Osenbrück ◽  
Hermann Rügner ◽  
...  
Keyword(s):  
Data Management ◽  
Collaborative Research ◽  
Large Scale ◽  
Full Range ◽  
Research Data ◽  
Environmental Research ◽  
Data Types ◽  
Research Data Management ◽  
Management Concepts

<p>The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management.We present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata.</p>

The challenging research data management support in the interdisciplinary cluster of excellence CliCCS

2020 ◽  
Author(s):  
Ivonne Anders ◽  
Andrea Lammert ◽  
Karsten Peters
Keyword(s):  
Data Management ◽  
Scientific Practice ◽  
Social Systems ◽  
Measurement Data ◽  
Research Data ◽  
Great Effort ◽  
Management Support ◽  
German Research Foundation ◽  
Research Data Management

<p>In 2019 the Universität Hamburg was awarded funding for 4 clusters of excellence in the Excellence Strategy of the Federal and State Governments. One of these clusters funded by the German Research Foundation (DFG) is “CliCCS – Climate, Climatic Change, and Society”. The scientific objectives of CliCCS are achieved within three intertwined research themes, on the Sensitivity and Variability in the Climate System, Climate-Related Dynamics of Social Systems, and Sustainable Adaption Scenarios. Each theme is structured into multiple projects addressing sub-objectives of each theme. More than 200 researchers the Hamburg University, but also other connected research centers and partner institutions  are involved and almost all of them are using but mainly produce new data.</p><p>Research data is produced with great effort and is therefore one of the valuable assets of scientific institutions. It is part of good scientific practice to make research data freely accessible and available in the long term as a transparent basis for scientific statements.</p><p>Within the interdisciplinary cluster of excellence CliCCS, the type of research data is very different. The data range from results from physically dynamic ocean and atmosphere models, to measurement data in the coastal area, to survey and interview data in the field of sociology. </p><p>The German Climate Computing Center (DKRZ) is taking care on the Research Data Management and supports the researchers in creating data management plans, keeping naming conventions or simply finding the optimal repository to publish the data. The goal is to store and long-term archiving of the data, but also to ensure the quality of the data and thus to facilitate potential reuse.</p>

The Minimum Mandatory Metadata Sets for the KIM Project and RAIDmap

2018 ◽  
pp. 391-412
Author(s):  
Alexander Ball ◽  
Mansur Darlington ◽  
Christopher McMahon
Keyword(s):  
Data Management ◽  
Information Management ◽  
Mechanical Engineering ◽  
Research Data ◽  
Minimal Set ◽  
Life Project ◽  
Research Data Management ◽  
Metadata Record

A Minimum Mandatory Metadata Set (M3S) was devised for the KIM (Knowledge and Information Management Through Life) Project to address two challenges. The first was to ensure the project's documents were sufficiently self-documented to allow them to be preserved in the long term. The second was to trial the M3S and supporting templates and tools as a possible approach that might be used by the aerospace, defence and construction industries. A different M3S was devised along similar principles by a later project called REDm-MED (Research Data Management for Mechanical Engineering Departments). The aim this time was to help specify a tool for documenting research data records and the associations between them, in support of both preservation and discovery. In both cases the emphasis was on collecting a minimal set of metadata at the time of object creation, on the understanding that later processes would be able to expand the set into a full metadata record.

Research Data Management in an Academic Library

2021 ◽  
pp. 38-55
Author(s):  
Chidi Onuoha Kalu ◽  
Esther Ihechiluru Chidi-Kalu ◽  
Titilola Abigail Mafe
Keyword(s):  
Data Management ◽  
Academic Libraries ◽  
Academic Library ◽  
Research Data ◽  
The Other ◽  
Research Data Management ◽  
Other Hand ◽  
Support Research

Academic libraries need to store, preserve, and manage scholars' intellectual output, hence the importance of research data management in academic libraries. This chapter focuses on research data management in academic libraries, and it aims at examining the concept of research data, which is referred to as the evidence used to inform or support research conclusions, while data management, on the other hand involves planning for and creating data, organizing, structuring, and documenting data, backing up and storing data, and preparing data for analysis to share with others or to preserve for the long-term.

scholarly journals Scientific Research Data Management for Soil-Vegetation-Atmosphere Data – The TR32DB

2012 ◽  
Vol 7 (2) ◽  
pp. 68-80 ◽  
Author(s):  
Constanze Curdt ◽  
Dirk Hoffmeister ◽  
Guido Waldhoff ◽  
Christian Jekel ◽  
Georg Bareth
Keyword(s):  
Data Management ◽  
Interdisciplinary Research ◽  
Management System ◽  
Scientific Research ◽  
Research Data ◽  
Data Management System ◽  
Research Data Management ◽  
Interdisciplinary Research Project ◽  
Practical Experiences

The implementation of a scientific research data management system is an important task within long-term, interdisciplinary research projects. Besides sustainable storage of data, including accurate descriptions with metadata, easy and secure exchange and provision of data is necessary, as well as backup and visualisation. The design of such a system poses challenges and problems that need to be solved.This paper describes the practical experiences gained by the implementation of a scientific research data management system, established in a large, interdisciplinary research project with focus on Soil-Vegetation-Atmosphere Data.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

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