The Alpine Environmental Data Analysis Centre (www.alpendac.eu) – A Component of the Virtual Alpine Observatory (VAO) (www.vao.bayern.de)

2020 ◽  
Author(s):  
Michael Bittner ◽  
Dominik Laux ◽  
Oleg Goussev ◽  
Sabine Wüst ◽  
Jana Handschuih ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Management ◽  
Heterogeneous Data ◽  
Research Data ◽  
Environmental Data ◽  
Research Network ◽  
Data Sets ◽  
Measurement Instruments ◽  
On Demand ◽  
Simulation Techniques

<p>The “Alpine Environmental Data Analysis Centre” (AlpEnDAC) is a research data management and analysis platform for research facilities around the Alps and similar mountain ranges. It provides the computational infrastructure for the Virtual Alpine Observatory (VAO), which is a research network of European high-altitude research stations (http://www.vao.bayern.de).</p><p> </p><p>Within the scope of previous work, the platform was developed with the focus on research data and metadata management as well as analysis and simulation tools. It offers the possibility to store and retrieve data securely (data-on-demand), to share it with other scientists and to interpret it with the help of computing-on-demand solutions via a user friendly web-based graphical user interface. The AlpEnDAC allows the analysis and consolidation of heterogeneous data sets from ground-based to satellite instruments.</p><p> </p><p>In a further development phase, launched on 1 August 2019, the existing services of the AlpEnDAC will be supplemented by new components in the fields of user support and quality assurance. Furthermore, the modelling and analysis software portfolio will be extended, focusing on the development of innovative services in the fields of service-on-demand and operating-on-demand as well as the integration of new data sources and measurement instruments.</p><p> </p><p>The AlpEnDAC helps environmental scientists to benefit from modern data management, data analysis, and simulation techniques. The VAO network, now including ten countries (Austria, France, Germany, Georgia, Italy, Norway, Slovenia, Switzerland, Bulgaria, and the Czech Republic) is an ideal and exciting context for developing the AlpEnDAC with researchers.</p><p> </p><p>This project receives funding from the Bavarian State Ministry of the Environment and Consumer Protection.</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.

Creating Tactile Graphs for Students With Visual Impairments

2019 ◽  
pp. 223-240
Author(s):  
Terence W. Cavanaugh ◽  
Nicholas P. Eastham
Keyword(s):  
Data Analysis ◽  
Rapid Prototyping ◽  
Visual Impairments ◽  
Research Data ◽  
Assistive Technologies ◽  
Data Sets ◽  
3D Printer ◽  
Graduate Schools ◽  
Student Research ◽  
The Cost

Educational technologists are often asked to provide assistance in the identification or creation of assistive technologies for students. Individuals with visual impairments attending graduate schools are expected to be able to work with data sets, including reading, interpreting, and sharing findings with others in their field, but due to their impairments may not be able to work with standard displays. The cost and time involved in preparing adapted graphs based on student research data for individuals with visual impairments can be prohibitive. This chapter introduces a method for the rapid prototyping of tactile graphs for students to use in data analysis through the use of spreadsheets, internet-based conversion tools, and a 3D printer.

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 Intelligent Integrative Platform for Sharing Heterogenuous Stem Cell Research Data

2021 ◽  
Author(s):  
Kirill Borziak ◽  
Irena Parvanova ◽  
Joseph Finkelstein
Keyword(s):  
Stem Cell ◽  
Cancer Stem Cell ◽  
Language Processing ◽  
Stem Cell Research ◽  
Heterogeneous Data ◽  
Research Data ◽  
Data Sets ◽  
Ease Of Access ◽  
Data Elements

Recent studies demonstrated that comparative analysis of stem cell research data sets originating from multiple studies can produce new information and help with hypotheses generation. Effective approaches for incorporating multiple diverse heterogeneous data sets collected from stem cell projects into a harmonized project-based framework have been lacking. Here, we provide an intelligent informatics solution for integrating comprehensive characterizations of stem cells with research subject and project outcome information. Our platform is the first to seamlessly integrate information from iPSCs and cancer stem cell research into a single platform, using a multi-modular common data element framework. Heterogeneous data is validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 103 published, publicly-available iPSC and cancer stem cell projects conducted in clinical, preclinical and in vitro evaluations. We validated the robustness of the platform, by seamlessly harmonizing diverse data elements, and demonstrated its potential for knowledge generation through the aggregation and harmonization of data. Future aims of this project include increasing the database size using crowdsourcing and natural language processing functionalities. The platform is publicly available at https://remedy.mssm.edu/.

Data management planning and repository demands for qualitative research

KWALON ◽  
2016 ◽  
Vol 21 (1) ◽  
Author(s):  
René van Horik
Keyword(s):  
Data Management ◽  
Research Data ◽  
Digital Data ◽  
Data Repository ◽  
Data Sets ◽  
Data Repositories ◽  
Management Planning ◽  
Research Activities ◽  
Trusted Data ◽  
New Research

Summary Nowadays, research without a role for digital data and data analysis tools is barely possible. As a result, we see an increasing interest in research data management, as this enables the replication of research outcomes and the reuse of research data for new research activities. Data management planning outlines how to handle data, both during research and after the research is completed. Trusted data repositories are places were research data are archived and made available for the long term. This article covers the state of the art concerning data management and data repository demands with a focus on qualitative data sets.

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 Archivists Managing Research Data? a Survey of Irish Organisations

2020 ◽  
Vol 15 (1) ◽  
pp. 9
Author(s):  
Rebecca Grant
Keyword(s):  
Data Analysis ◽  
Data Management ◽  
Survey Methods ◽  
Research Data ◽  
Research Data Management ◽  
Management Policies ◽  
The Impact ◽  
Policies And Practices

This paper describes a survey undertaken in 2017 to establish which research data management policies and practices were in place at Irish organisations; the extent to which archivists and records managers were employed to manage research data at those organisations; and the impact that archival skills have on research data management at an organisation. The paper describes the survey methods and data analysis, and presents findings including the presence of archivists and records managers at more than half of the surveyed organisations. Next steps for the research are also outlined.

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 Foundational Practices of Research Data Management

2020 ◽  
Vol 6 ◽  
Author(s):  
Kristin Briney ◽  
Heather Coates ◽  
Abigail Goben
Keyword(s):  
Data Analysis ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
Data Handling ◽  
Good Data ◽  
Research Data Management ◽  
The Future

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.

scholarly journals Machine learning, artificial neural networks and social research

2020 ◽  
Author(s):  
Giovanni Di Franco ◽  
Michele Santurro
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Analysis ◽  
Social Research ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Artificial Neural ◽  
Analysis Models ◽  
The Impact

Abstract Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different disciplines such as mathematics, statistics, computer science and neuroscience. This approach is characterized by the use of algorithms to extract knowledge from large and heterogeneous data sets. In addition to offering a brief introduction to ANN algorithms-based ML, in this paper we will focus our attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures. In this regard, we will provide three examples of applications on sociological data to assess the impact of ML in the study of relationships between variables. Finally, we will compare the potential of ML with traditional data analysis models.

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