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Possible data base applications in social research include working with quantitative data files and gathering research information. Social research commonly involves 10 data management activities common to most major mainframe statistical packages. Social researchers using microcomputers may need other solutions for their data management needs. These include writing a program, using a standard spreadsheet, and using a data base manager program. Microcomputers and their data base software can increase the effectiveness and efficiency of information gathering for research projects by easing and improving notetaking and organizing and by providing highly efficient, error-checking tools to assist in entry of research data from many different kinds of sources. Mainframe and minicomputers traditionally have not been used for many of these information gathering activities. For the nearfuture, microcomputer data base managers will continue to serve useful purposes for social researchers, even while they remain not completely adequate because of their orientation toward their principal market: business people. Developments in the software arena will help make microcomputer data base manage ment tools more useful. As microcomputer statistical packages improve, social researchers may once again do most of their "data base management "using the same tool they use to perform their statistical analysis.

This policy details how the NT AHKPI system and data will be managed, maintained and protected in strict accordance with national and NT information privacy legislation and standards. ; Y

This document includes the deliverable D7.2 of the AiRT project, which covers the first version of the Data Management Plan. Consortium of AiRT project participates in the Open Research Data Pilot for H2020 programme. Therefore, this document will describe how research data collected during the project will be made accessible by the partners and the repositories selected where deposit the data. Document elaboration is based on information included in deliverable D6.3 (Report on monitoring and evaluation of communication and dissemination activities). Revisions for the Data Management Plan will be made during the rest of the project, to include information related to next deliverables and tasks. This deliverable is led by UPV, although all the partners participate in the compliance of the task. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement nº 732433.

The changing financial services landscape -- Taxonomy of financial data -- Information as the fuel for financial services' business processes -- Challenges and trends in the financial data management agenda -- Data management tools and techniques -- Data management processes and quality management -- Data management organization -- What's next?

The data management plan "describes the data management life cycle for the data to be collected, processed and/or generated by"1 ESiWACE2 in all relevant work packages. It will be updated throughout the project period in time with the periodic project evaluation. With ESiWACE2 focussing on software development and the preparation of the weather and climate communities for exascale computing in terms of services and infrastructural means, only few actual "research data" are created in the project. They can be basically grouped into three categories: (1) model output for development purposes, (2) performance analysis and benchmark data, and (3) educational material. ; ESiWACE2 has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 823988

Research carried out in the SSH occurs across a wide array of disciplines and languages. While this specialization makes it possible to investigate a bewildering range of different topics, it also leads to a fragmentation that prevents SSH research from reaching its full potential. Use and reuse of SSH research is not as high as one might desire it to be, interdisciplinary collaboration possibilities are often missed, and as a result, the societal impact of this research can often be limited. TRIPLE strives to address these issues. With a consortium of 19 partners, TRIPLE proposes an integrated multilingual and multicultural solution for the appropriation of SSH resources. The TRIPLE platform will seek to provide an enhanced discovery experience thanks, in large part, to the linked exploration functionalities provided by the ISIDORE search engine, developed and maintained by CNRS-HumaNum 1. TRIPLE aims to be a coherent solution providing innovative tools to support Research (including tools for visualisation, annotation, trust building system, crowdfunding, social network and recommender system). Moreover TRIPLE will propose new ways to conduct and to discover research and will connect researchers, consortiums and institutions with other stakeholders (citizens, policy makers, companies) enabling them to formulate and articipate in research projects and respond to other issues. TRIPLE will be a dedicated service of OPERAS RI and seeks to become a strong service in the EOSC marketplace. ; The Report awaits official approval by the EU Project Officer. --- The TRIPLE project (https://www.gotriple.eu/), which is financed under the Horizon 2020 framework (https://cordis.europa.eu/project/id/863420), under Grant Agreement No. 863420, with approx. 5.6 million Euros for a duration of 42 months (2019-2023). --- At the heart of the project is the development of the TRIPLE platform, an innovative multilingual and multicultural discovery solution.

This document provides a brief summary of data recovery excavations conducted by Chicora Foundation for Kiawah Partners of Charleston, SC at archaeological site 38CH1220, a Union Civil War encampment, under an existing Army Corps Memorandum of Agreement (MOA) and supplemented by an Office of Coastal Resource Management (OCRM) MOA approved on April 5, 2012. The work was based on a data recovery plan submitted by Chicora archaeologists and approved by the State Historic Preservation Office in 2011.

euCanSHare deliverable D3.1, the first version of the projects Data Management Plan. Executive Summary: This Data Management Plan addresses the purpose and description of data handled within the euCanSHare project and the model for data handling during and after the project, including provisions for data collection, secure long-term storage, integration and interoperability, accessibility and exploitation, in compliance with the principles for findable, accessible, interoperable and reusable research data. ; This deliverable has been produced in the context of the euCanSHare ("An EU-Canada joint infrastructure for next-generation multi-Study Heart research") Research and Innovation Action, funded by the European Union's Horizon 2020 programme (grant agreement No 825903), the Canadian Institutes of Health Research (CIHR) and the Fonds de recherche du Québec – Santé under the framework of Canada‐EU Commission Flagship Collaboration for Human data storage, integration and sharing.

"This paper summarizes the findings of an analysis among scientific infrastructure service providers. These service providers have been evaluated in regard to their potential services for the management of publication-related research data. By conducting a desk research and an online survey, we found out that almost three quarters of all responding research data centres, archives and libraries generally store externally generated research data – what also applies to publication-related data. Almost 75% of all respondents also store and host the code of computation (the syntax of statistical analyses). If self-written software components have been used to generate research outputs, only 40% of all respondents accept these software components for storing and hosting. Eight in ten institutions also stated that they are taking specific
actions for digital long-term preservation of their data. In regard to the documentation
of stored and hosted research data almost 70% of all respondents claimed to use the metadata schema of the Data Documentation Initiative (DDI); Dublin Core was used by 30 percent (multiple answers were permitted). Almost two thirds also used persistent identifiers to facilitate citation of these datasets. Three in four respondents also stated to support researchers in creating metadata for their data. Application programming interfaces (APIs) for uploading or searching datasets currently have not been implemented by any of the respondents yet. Little widespread is the use of semantic technologies like RDF." [author's abstract]

The Europe 2020 strategy for a smart, sustainable, and inclusive economy underlines the central role of knowledge and innovation in generating growth. For this reason, the European Union strives to improve access to scientific information and to boost the benefits of public investment in the research funded under the EU Framework Programme for Research and Innovation Horizon 2020 (2014-2020). According to this strategy, in Horizon 2020 a limited pilot action on open access to research data has been implemented so that participating projects will be required to develop an Open Data Management Plan (ODMP), in which they will specify what data will be open. This updated version of the deliverable of the Q-SORT project is prepared as part of WP1, Open Data Management Plan (Update). In this document we update the discussion regarding the management, life cycle, processes of data generated by Q-SORT in order to make such data findable, accessible, interoperable, and reusable (FAIR). The aim of this document is to provide an analysis of the main elements of the data management policy that will be used with regard to all the data sets that will be generated by the project. In particular, the deliverable outlines how research data will be handled during the lifetime of Q-SORT and describes what data will be collected, processed or generated and what methodology will be followed, whether and how this data will be shared and/or made open, and how it will be curated and preserved. This data management plan is intended as a dynamic document that will be edited and updated during the project.

CO-OPERAS (https://www.go-fair.org/implementation-networks/overview/co-operas/) and SSHOC (https://sshopencloud.eu/) share a similar task: supporting researchers in the social sciences and humanities to integrate their work and results according to the FAIR principles. To this end, SSHOC and CO-OPERAS organised a joint workshop revolving around FAIR principles for research data in the SSH on the one hand and the development of the SSH Open Marketplace on the other. They asked participants to talk about their needs and experiences with the FAIR principles in their own research realities and then, not independently from this discussion, to give feedback about the conception of the SSHOC Open Marketplace. The idea of organizing a series of workshops in small local settings and in native languages - in this case in German - in order to anchor and align the FAIR principles with individual research practices was coming from the CO-OPERAS GO-FAIR Implementation Network. Already followed by a series of events in Italy (Turin), Portugal (Coimbra), and France (Marseilles), the concept has been proven in Göttingen as well: the uptake of the workshop was, with 28 participants from across the whole country, very good. The German language approach considerably lowered the threshold to give feedback and formulate own ideas. The first part of the workshop was dedicated to the FAIR principles following the concept and structure of the previous CO-OPERAS workshops. Like in the previous events, participants with different research background (history, art history, theology, political science, Romance studies, German studies, Japanese studies, archaeology, cultural anthropology, coptology, philosophy) discussed the following complexes of questions: What are the research data in your discipline and what role does research data management play for your academic work? How the FAIR principles are aligned with your data collection practices? How the FAIR principles are aligned with your data processing practices? How the FAIR principles ...

Data Development for International Research (DDIR) DDIR II: Event Data Research

ADR UK is helping to transform the way researchers access the UK's wealth of administrative data, enabling government policy to be informed by the best evidence available. Emma shares her insights into the ADR UK approach to making this happen, explaining why building trust is central to the ADR UK mission.