Towards a Specialized Environmental Data Portal: Challenges and Opportunities

2020 ◽  
Author(s):  
Ionut Iosifescu-Enescu ◽  
Gian-Kasper Plattner ◽  
Dominik Haas-Artho ◽  
David Hanimann ◽  
Konrad Steffen
Keyword(s):  
Data Management ◽  
Scientific Information ◽  
Central Element ◽  
Research Data ◽  
Environmental Data ◽  
Research Data Management ◽  
Environmental Domain ◽  
Federal Institute ◽  
Wide Range ◽  
Data Portal

<p>EnviDat – www.envidat.ch – is the institutional Environmental Data portal of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. Launched in 2012 as a small project to explore possible solutions for a generic WSL-wide data portal, it has since evolved into a strategic initiative at the institutional level tackling issues in the broad areas of Open Research Data and Research Data Management. EnviDat demonstrates our commitment to accessible research data in order to advance environmental science.</p><p>EnviDat actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles. Core EnviDat research data management services include the registration, integration and hosting of quality-controlled, publication-ready data from a wide range of terrestrial environmental systems, in order to provide unified access to WSL’s environmental monitoring and research data. The registration of research data in EnviDat results in the formal publication with permanent identifiers (EnviDat own PIDs as well as DOIs) and the assignment of appropriate citation information.</p><p>Innovative EnviDat features that contribute to the global system of modern documentation and exchange of scientific information include: (i) a DataCRediT mechanism designed for specifying data authorship (Collection, Validation, Curation, Software, Publication, Supervision), (ii) the ability to enhance published research data with additional resources, such as model codes and software, (iii) in-depth documentation of data provenance, e.g., through a dataset description as well as related publications and datasets, (iv) unambiguous and persistent identifiers for authors (ORCIDs) and, in the medium-term, (v) a decentralized “peer-review” data publication process for safeguarding the quality of available datasets in EnviDat.</p><p>More recently, the EnviDat development has been moving beyond the set of core features expected from a research data management portal with a built-in publishing repository. This evolution is driven by the diverse set of researchers’ requirements for a specialized environmental data portal that formally cuts across the five WSL research themes forest, landscape, biodiversity, natural hazards, and snow and ice, and that concerns all research units and central IT services.</p><p>Examples of such recent requirements for EnviDat include: (i) immediate access to data collected by automatic measurements stations, (ii) metadata and data visualization on charts and maps, with geoservices for large geodatasets, and (iii) progress towards linked open data (LOD) with curated vocabularies and semantics for the environmental domain.</p><p>There are many challenges associated with the developments mentioned above. However, they also represent opportunities for further improving the exchange of scientific information in the environmental domain. Especially geospatial technologies have the potential to become a central element for any specialized environmental data portal, triggering the convergence between publishing repositories and geoportals. Ultimately, these new requirements demonstrate the raised expectations that institutions and researchers have towards the future capabilities of research data portals and repositories in the environmental domain. With EnviDat, we are ready to take up these challenges over the years to come.</p>

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 NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

scholarly journals The Data Management Skills Support Initiative: Synthesising Postgraduate Training in Research Data Management

2012 ◽  
Vol 7 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Laura Molloy ◽  
Kellie Snow
Keyword(s):  
Data Management ◽  
Management Training ◽  
Skills Training ◽  
Research Data ◽  
Management Skills ◽  
Research Information ◽  
Research Data Management ◽  
Researcher Development ◽  
Development Framework ◽  
Wide Range

This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.

scholarly journals Fostering Open Science at WSL with the EnviDat Environmental Data Portal

2018 ◽  
Author(s):  
Ionut Iosifescu Enescu ◽  
Marielle Fraefel ◽  
Gian-Kasper Plattner ◽  
Lucia Espona-Pernas ◽  
Dominik Haas-Artho ◽  
...  
Keyword(s):  
Open Science ◽  
Research Data ◽  
Environmental Data ◽  
Data Sets ◽  
Institutional Research ◽  
Data Set ◽  
Digital Resources ◽  
Federal Institute ◽  
Data Policy ◽  
Data Portal

EnviDat is the institutional research data portal of the Swiss Federal Institute for Forest, Snow and Landscape WSL. The portal is designed to provide solutions for efficient, unified and managed access to the WSL’s comprehensive reservoir of monitoring and research data, in accordance with the WSL data policy. Through EnviDat, WSL is fostering open science, making curated, quality-controlled, publication-ready research data accessible. Data producers can document author contributions for a particular data set through the EnviDat-DataCRediT taxonomy. The publication of research data sets can be complemented with additional digital resources, such as, e.g., supplementary documentation, processing software or detailed descriptions of code (i.e. as Jupyter Notebooks). The EnviDat Team is working towards generic solutions for enhancing open science, in line with WSL’s commitment to accessible research data.

scholarly journals Research Data Management at 9 Universities in Baden-Wuerttemberg, Germany. The Results from the Final Report of the bwFDM Communities Project

2015 ◽  
Author(s):  
Karlheinz Pappenberger
Keyword(s):  
Data Management ◽  
Research Data ◽  
The State ◽  
Final Report ◽  
Full Time ◽  
It Service ◽  
Research Data Management ◽  
User Stories ◽  
Wide Range ◽  
The Status

See video of the presentation.On 17th July 2015 the Ministry of Science, Research and the Arts for Baden-Wuerttemberg, Germany, invited national experts to the presentation of the final report of the ‘bwFDM communities’ project. This 18 month project was launched at the beginning of 2014 to evaluate the needs of services and the support that libraries and IT service centres should offer researchers in the area of research data management. Full-time key project staff had been established at all 9 universities in the state of Baden-Wuerttemberg to conduct semi-structured personal interviews of all research groups working with research data (in a broad sense including all areas of science, social science and humanities) and to document them in the form of user stories. 627 interviews have been conducted and more than 2,500 user stories could be extracted, showing the wide range of needs and wishes articulated by researchers. On this basis issues of importance and requirements had be identified, categorised in 18 different groups and finalised into an analysis of the status quo and recommendations for concrete action plans. The results cover the areas ‘general requirements and policy framework’, ‘data collection and data sharing’, ‘technical framework and virtual research environments’, ‘preservation’,  ‘IT infrastructure and IT support’, ‘licencing’ and ‘Open Science’.The presentation will give an overview of the project results and will highlight the roles libraries and IT service centres are expected to play from the researcher´s point of view.As the final report to the Ministry contributes to a comprehensive research data management strategy for the State of Baden-Wuerttemberg, the presentation will also point out the status of the federal strategy in RDM.

scholarly journals NFDI4BioDiversity: Biodiversity, ecology and environmental data

2019 ◽  
Vol 3 ◽  
Author(s):  
Frank Oliver Glöckner ◽  
Michael Diepenbroek
Keyword(s):  
Genetic Diversity ◽  
Data Management ◽  
Data Centers ◽  
Research Data ◽  
Biological Data ◽  
Environmental Data ◽  
User Community ◽  
Management Platform ◽  
Research Data Management ◽  
The Status

Background: The NFDI process in Germany The digital revolution is fundamentally transforming research data and methods. Mastering this transformation poses major challenges for stakeholders in the domains of science and policy. The process of digitalisation creates immense opportunities, but it must be structured proactively. To this end, the establishment of effective governance mechanisms for research data management (RDM) is of fundamental importance and will be one key driver for successful research and innovation in the future. In 2016 the German Council for Information Infrastructures (RfII) recommended the establishment of a “Nationale Forschungsdateninfrastruktur” (National Research Data Infrastructure, or NFDI), which will serve as the backbone for research data management in Germany. The NFDI should be implemented as a dynamic national collaborative network that grows over time and is composed of various specialised nodes (consortia). The talk will provide a short overview of the status and objectives of the NFDI. It will commence with a description of the goals of the NFDI4BioDiversity consortium which was established for the targeted support of the biodiversity community with data management. The NFDI4BioDiversity Consortium: Biodiversity, Ecology & Environmental Data Biodiversity is more than just the diversity of living species. It includes genetic diversity, functional diversity, interactions and the diversity of whole ecosystems. Mankind continuous to dramatically impact the earth’s ecosystem: species dying-out genetic diversity as well as whole ecosystems are endangered or already lost. Next to the loss of charismatic species and conspicuous change in ecosystems, we are experiencing a quiet loss of common species which together has captured high level policy attention. This has impacts on vital ecosystem services that provide the foundation of human well-being. A general understanding of the status, trends and drivers of the biodiversity on earth is urgently needed to devise conservation responses. Besides the fact that data are often scattered across repositories or not accessible at all, the main challenge for integrative studies is the heterogeneity of measurements and observation types, combined with a substantial lack of documentation. This leads to inconsistencies and incompatibilities in data structures, interfaces and semantics and thus hinders the re-usability of data to answer scientifically and socially relevant questions. Synthesis as well as hypothesis generation will only proceed when data are compliant with the FAIR (Findable, Accessible, Interoperable and Re-usable) data principles. Over the last five years these key challenges have been addressed by the DFG funded German Federation for Biological Data (GFBio) project. GFBio encompasses technical, organizational, financial, and community aspects to raise awareness for research data management in biodiversity research and environmental sciences. To foster sustainability across this federated infrastructure the not-for-profit association “Gesellschaft für biologische Daten e.V. (GFBio e.V.)” has been set up in 2016 as an independent legal entity. NFDI4BioDiversity builds on the experience and established user community of GFBio and takes advantage of GFBio e.V. GFBio already comprises data centers for nucleotide and environmental data as well as the seven well-established data centers of Germany´s largest natural science research facilities, museums and world’s most diverse microbiological resource collection. The network is now extended to include the network of botanical gardens and the largest collections of crop plants and their wild relatives. All collections together host more than 75% of all museum objects (150 millions) in Germany and >80% of all described microbial species. They represent the biggest and internationally-relevant data repositories. NFDI4BioDiversity will extend its community engagement at the science-society-policy interface by including farm animal biology, crop sciences, biodiversity monitoring and citizen science, as well as systems biology encompassing world-leading tools and collections for FAIR data management. Partners of the German Network for Bioinformatics Infrastructure (de.NBI) provide large scale data analysis and storage capacities in the cloud, as well as extensive continuous training and education experiences. Dedicated personnel will be responsible for the mutual exchange of data and experiences with NFDI4Life-Umbrella,NFDI4Earth, NFDI4Chem, NFDI4Health and beyond. As digitalization and liberation of data proceeds, NFDI4BioDiversity will foster community standards, quality management and documentation as well as the harmonization and synthesis of heterogeneous data. It will pro-actively engage the user community to build a coordinated data management platform for all types of biodiversity data as a dedicated added value service for all users of NFDI.

scholarly journals Exploring research data management planning challenges in practice

2020 ◽  
Vol 62 (1) ◽  
pp. 29-37
Author(s):  
Armel Lefebvre ◽  
Baharak Bakhtiari ◽  
Marco Spruit
Keyword(s):  
Data Management ◽  
Scientific Information ◽  
Public Funding ◽  
Research Data ◽  
High Quality ◽  
Management Planning ◽  
Research Data Management ◽  
Barriers To Access ◽  
Funding Agencies ◽  
Management Plans

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

A Lightweight, Microservice-Based Research Data Management Architecture for Large Scale Environmental Datasets

2020 ◽  
Author(s):  
Alexander Götz ◽  
Johannes Munke ◽  
Mohamad Hayek ◽  
Hai Nguyen ◽  
Tobias Weber ◽  
...  
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Virtual Water ◽  
Research Data ◽  
Environmental Data ◽  
Data Repositories ◽  
Research Data Management ◽  
Water Value ◽  
Simulation Based ◽  
Core Components

<p>LTDS ("Let the Data Sing") is a lightweight, microservice-based Research Data Management (RDM) architecture which augments previously isolated data stores ("data silos") with FAIR research data repositories. The core components of LTDS include a metadata store as well as dissemination services such as a landing page generator and an OAI-PMH server. As these core components were designed to be independent from one another, a central control system has been implemented, which handles data flows between components. LTDS is developed at LRZ (Leibniz Supercomputing Centre, Garching, Germany), with the aim of allowing researchers to make massive amounts of data (e.g. HPC simulation results) on different storage backends FAIR. Such data can often, owing to their size, not easily be transferred into conventional repositories. As a result, they remain "hidden", while only e.g. final results are published - a massive problem for reproducibility of simulation-based science. The LTDS architecture uses open-source and standardized components and follows best practices in FAIR data (and metadata) handling. We present our experience with our first three use cases: the Alpine Environmental Data Analysis Centre (AlpEnDAC) platform, the ClimEx dataset with 400TB of climate ensemble simulation data, and the Virtual Water Value (ViWA) hydrological model ensemble.</p>

scholarly journals NRDC Data Visualization Web Suite

10.29007/rkqh ◽  
2020 ◽  
Author(s):  
Andrew Muñoz ◽  
Frederick Harris ◽  
Sergiu Dascalu
Keyword(s):  
Data Management ◽  
Data Visualization ◽  
Data Center ◽  
Web Application ◽  
Web Applications ◽  
Research Data ◽  
The State ◽  
Environmental Data ◽  
High Rate ◽  
Research Data Management

The Nevada Research Data Center (NRDC) is a research data management center that collects sensor-based data from various locations throughout the state of Nevada. The measurements collected are specifically environmental data, which are used in cross-disciplinary research across different facilities. Since data is being collected at a high rate, it is necessary to be able to visualize the data quickly and efficiently. This paper discusses in detail a web application that can be used by researchers to make visualizations that can help in data comparisons. While there exist other web applications that allows researchers to visualize the data, this project expands on that idea by allowing researchers the ability to not only visualize the data but also make comparisons and predictions.

scholarly journals Fostering Open Science at WSL with the EnviDat Environmental Data Portal

2018 ◽  
Author(s):  
Ionut Iosifescu Enescu ◽  
Marielle Fraefel ◽  
Gian-Kasper Plattner ◽  
Lucia Espona-Pernas ◽  
Dominik Haas-Artho ◽  
...  
Keyword(s):  
Open Science ◽  
Research Data ◽  
Environmental Data ◽  
Data Sets ◽  
Institutional Research ◽  
Data Set ◽  
Digital Resources ◽  
Federal Institute ◽  
Data Policy ◽  
Data Portal

EnviDat is the institutional research data portal of the Swiss Federal Institute for Forest, Snow and Landscape WSL. The portal is designed to provide solutions for efficient, unified and managed access to the WSL’s comprehensive reservoir of monitoring and research data, in accordance with the WSL data policy. Through EnviDat, WSL is fostering open science, making curated, quality-controlled, publication-ready research data accessible. Data producers can document author contributions for a particular data set through the EnviDat-DataCRediT taxonomy. The publication of research data sets can be complemented with additional digital resources, such as, e.g., supplementary documentation, processing software or detailed descriptions of code (i.e. as Jupyter Notebooks). The EnviDat Team is working towards generic solutions for enhancing open science, in line with WSL’s commitment to accessible research data.

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