The dataset is extracted from tlocal EHR System(Cerner Millenium) containing demographics, administrative hospital encounter information, previous medical history (diagnoses and procedures), current symptoms and disease complications, and clinical events.

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.

The NIST RDaF is a map of the research data space that uses a lifecycle approach with six high-level lifecycle stages to organize key information concerning research data management (RDM) and research data dissemination. Through a community-driven and in-depth process, stakeholders identified topics and subtopics?programmatic and operational activities, concepts, and other important factors relevant to RDM. All elements of the RDaF framework foundation?the lifecycle stages and their associated topics and subtopics?are defined. Most subtopics have several informative references, which are resources such as guidelines, standards, and policies that assist stakeholders in addressing that subtopic. Further, the NIST RDaF team identified 14 Overarching Themes which are pervasive throughout the framework. The Framework foundation enables organizations and individual researchers to use the RDaF for self-assessment of their RDM status. The RDaF includes sample ?profiles? for various job functions or roles, each containing topics and subtopics that an individual in the given role is encouraged to consider in fulfilling their RDM responsibilities. Individual researchers and organizations involved in the research data lifecycle can tailor these profiles for their specific job function using a tool available on the RDaF website. The methodologies used to generate all features of the RDaF are described in detail in the publication NIST SP 1500-8.This database version of the NIST RDaF is designed so that users can readily navigate the various lifecycle stages, topics, subtopics, and overarching themes from numerous locations. In addition, unlike the published text version, links are included for the definitions of most topics and subtopics and for informative references for most subtopics. For more information on the database, please see the FAQ page.

The Third Sector Research Centre carried out research on the key issues affecting charities and voluntary organisations, community groups, social enterprises, cooperatives and mutuals.

This specific data collection contains organisational characteristics and financial history information from UK-based charities, non-charity organisation, mutual societies and co-operative organisations. Organisational characteristics include charities' area of operation, area of benefit and standard industrial classification 2007 system code (SIC07). These characteristics were extracted from: (1) information held by the Charity Commission for charities registered with the Charity Commission for England and Wales, (2) information held by the Office of the Scottish Charity Regulator (OSCR) for Scottish charities, (3) information on co-operative organisations operating in the UK held by CoopsUK, (4) information on Mutual Societies provided by the Financial Conduct Authority (these include Industrial and Provident Societies, credit unions, building societies and friendly societies). The dataset also contains cross-matches between organisations found in the different lists of these bodies. The financial history data contains income and expenditure data for charities registered with the Charity Commission for the period 1985 - 2006.

A further five data collections resulting from research by the Third Sector Research Centre can be accessed via related resources.

The Third Sector Research Centre (TSRC) is a Venture Funded Centre, in partnership with the Office for Civil Society and the Barrow Cadbury Trust. TSRC is based at the University of Birmingham, but is a joint venture with the University of Southampton, with additional contributions from Middlesex and Kent. Third sector policy has now been devolved to the separate administrations in Scotland, Wales and Northern Ireland and research includes analysis of the impact of this devolution. TSRC employs a Knowledge Exchange Team to ensure that its work has a direct impact on policy and practice. Research activity includes: (1) Analysis of theoretical issues and development of a critical understanding of the policy environment for the sector; (2) Securing and analysing reliable data about the size, shape, structure and dynamics of the sector - including quantitative data, drawn from administrative sources and large scale surveys, and in-depth qualitative research with a selection of sector organisations; (3) Analysis of the economic and social impact of the sector, including the role of the sector in delivering public services, the development of social enterprise, the role of organisations in the environmental field, and the mapping and analysis of smaller community organisations operating 'below-the-radar'.

The aim of this survey was to collect feedback about existing training programmes in research data management for researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate students across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2016. 191 responses were received.

This archive publishes and preserves short and long-term research data collected from studies funded by:Forest Service Research and Development (FS R&D)Joint Fire Science Program (JFSP)Aldo Leopold Wilderness Research Institute (ALWRI)Of special interest, our collection includes data from a number of Forest Service Experimental Forests and Ranges.Each archived data set (i.e., 'data publication') contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.

Public opinion poll on political attitudes in Scotland Quota sample Face-to-face interview

An overview of practical, free, online resources and tools that you can begin using today to incorporate research data management into your practice of librarianship from the Libraries for Research Data Interest Group of the Research Data Alliance. Translated from English into German.

Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided. Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

A data warehouse that integrates information on patients from multiple sources and consists of patient information from all the visits to Cincinnati Children''''s between 2003 and 2007. This information includes demographics (age, gender, race), diagnoses (ICD-9), procedures, medications and lab results. They have included extracts from Epic, DocSite, and the new Cerner laboratory system and will eventually load public data sources, data from the different divisions or research cores (such as images or genetic data), as well as the research databases from individual groups or investigators. This information is aggregated, cleaned and de-identified. Once this process is complete, it is presented to the user, who will then be able to query the data. The warehouse is best suited for tasks like cohort identification, hypothesis generation and retrospective data analysis. Automated software tools will facilitate some of these functions, while others will require more of a manual process. The initial software tools will be focused around cohort identification. They have developed a set of web-based tools that allow the user to query the warehouse after logging in. The only people able to see your data are those to whom you grant authorization. If the information can be provided to the general research community, they will add it to the warehouse. If it cannot, they will mark it so that only you (or others in your group with proper approval) can access it.

BackgroundThere is increasing recognition of the importance of sharing research data within the international scientific community, but also of the ethical and social challenges this presents, particularly in the context of structural inequities and varied capacity in international research. Public involvement is essential to building locally responsive research policies, including on data sharing, but little research has involved stakeholders from low-to-middle income countries.MethodsBetween January and June 2014, a qualitative study was conducted in Kenya involving sixty stakeholders with varying experiences of research in a deliberative process to explore views on benefits and challenges in research data sharing. In-depth interviews and extended small group discussions based on information sharing and facilitated debate were used to collect data. Data were analysed using Framework Analysis, and charting flow and dynamics in debates.FindingsThe findings highlight both the opportunities and challenges of communicating about this complex and relatively novel topic for many stakeholders. For more and less research-experienced stakeholders, ethical research data sharing is likely to rest on the development and implementation of appropriate trust-building processes, linked to local perceptions of benefits and challenges. The central nature of trust is underpinned by uncertainties around who might request what data, for what purpose and when. Key benefits perceived in this consultation were concerned with the promotion of public health through science, with legitimate beneficiaries defined differently by different groups. Important challenges were risks to the interests of study participants, communities and originating researchers through stigmatisation, loss of privacy, impacting autonomy and unfair competition, including through forms of intentional and unintentional 'misuse' of data. Risks were also seen for science.DiscussionGiven background structural inequities in much international research, building trust in this low-to-middle income setting includes ensuring that the interests of study participants, primary communities and originating researchers will be promoted as far as possible, as well as protected. Important ways of building trust in data sharing include involving the public in policy development and implementation, promoting scientific collaborations around data sharing and building close partnerships between researchers and government health authorities to provide checks and balances on data sharing, and promote near and long-term translational benefits.

No description was included in this Dataset collected from the OSF

This dataset was used for the research article "Open research data: a case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse", published in the Journal of Librarianship & Information Science.

The data entails:

1. Coded interview transcripts, available as either .atlproj9- or .qdpx-file
2. Non-coded interview transcripts of interviews 1-4 (policy makers and support staff), available as either .docx- or .xml-file
3. Non-coded interview transcripts of interviews 5-7 (researchers), available as either .docx- or .xml-file
4. Codebook, available as either .xlsx- or .csv-file

The file contents per item are in principle the same; only the filetype differs.

This presentation provides an update on the status of research data preservation initiatives in Canada.

UNEKE is a joint project of the University of Duisburg-Essen and the RWTH Aachen University funded by the Federal Ministry of Education and Research. Under the leadership of the University Library Duisburg-Essen, the project aims to develop a strategic approach for a future-oriented research data management for institutions. UNEKE conducted a survey to determine both current storage practices and storage needs at universities. Throughout Germany, 13 universities took part in the survey. Especially in the accompanying scientific study, UNEKE aimed at the question of what keeps researchers busy and encourages them to publish and share data in the future. In order to identify the hindering and supporting factors in the practice of open data in higher education, a large-scale study with an online questionnaire was designed and carried out. The aim of the quantitative method was to collect descriptive knowledge about the status quo of researchers on the practices and requirements of research data management and to test and validate the presented research model. Therefore, the data set was divided into two parts: In the first part (part one), the participants were asked descriptive questions about their storage behaviour; in the second part (part two), scientifically valid constructs were used to learn more about the users´ attitudes towards data exchange. In this context, several predictors of data exchange behaviour are measured, including perceived (dis)benefits and risk factors. Part one of the survey was available in two languages, German and English. The second part consisted of different instruments, either taken from already validated instruments or slightly modified. All instruments used were originally published in English. In order to avoid undesired effects from the translation, the English form of the instruments was retained as in the original and the second part of the questionnaire was asked for in English. The study focuses on university members from Germany. The data set contains data from members of ten large universities (over 20,000 members) and three medium-sized universities (over 10,000 members). Due to internal requirements, the survey did not start at all participating universities at the same time, so that the survey will run between March 2018 and January 2019.
Part 1: Descriptive questionnaire

Topics: 1. background research project: duration of a typical research project (in months); generated data volume of a typical research project (gigabyte); percentage of the data volume in the initial phase, the intermediate phase and the final phase of the research project; storage location of the research data (e.g. at an external data centre, centrally on a server of the university, locally on the official computer, etc.); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); data volume of a typical research project (gigabyte); percentage of total data with which the respondent works regularly (in the first, second and last third of the research project); percentage of total data used and processed by several persons; number of persons working together on a typical research project; percentage of total data volume which, in the sense of good scientific practice, is 10 years or more 25 years should be stored (in the first, second and last third of the research project); type of archiving of own frog data for at least 25 years; expertise in the institute/department with systems or tools that support research data management.

1. General: data sources (e.g. observations and experiments, surveys and interviews, meta-studies, etc.); type of data formats; server administration, file server, backup and/or archive available at the faculty (data storage, content data maintenance); hours for server administration; importance of data protection; anonymisation of data in the course of the research process; guidelines or recommendations for handling research data; guidelines to fulfil; access to own data; protocol on data access; importance of regulation of data access; external persons or institutions who need to gain access to data; publication of raw data after first use; metadata collected; schema or standard for collecting metadata.

Demography: faculty; position; department; self-assessment knowledge of research data management; problems related to research data (e.g. outdated data formats, etc.); desired services of the university in handling research data

Additionally coded were: Interview number; university or...

Researchers seeking to share their data with coordinating centers such as the National Database for Autism Research (NDAR), face numerous barriers to establishing new connections and maintaining existing ones. We sought to dramatically reduce the time and money required to establish and maintain the interoperability of data between research centers, by establishing a process where manual recoding of data is replaced by data sharing instructions in the form of extraction and transformation scripts. Over the course of seven typical (20-60 subjects, 400-1000 fields each) data submissions to NDAR, the need for duplication, retranscription, or restructuring of the source data was fully eliminated. Separating the extraction and transformation scripts from data files also eradicated the impact of additional data collection on the time required to repeat successful transmissions. Revision controlled management of these scripts also provided a new benefit: traceability of the transformation process itself. Now, point-in-time retrieval of extraction scripts and explanations for modifications to the data sharing interface are possible. This method has proven to be successful and efficient for interfacing research data with NDAR. It presents little-to-no impact to transmitting investigators’ data, ensures high data integrity, trivializes the complexities of repeatedly modifying a growing dataset over time, and introduces traceability to the collaborative process of integrating two collections of data with one another.

Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

This dataset represents an inventory of research data services at 120 US colleges and universities. The data was collected using a systematic web content analysis process in late 2019. This dataset underlies the following report: Jane Radecki and Rebecca Springer, "Research Data Services in US Higher Education: A Web-Based Inventory," Ithaka S+R, Nov. 2020, https://doi.org/10.18665/sr.314397.

We defined research data services as any concrete, programmatic offering intended to support researchers (including faculty, postdoctoral researchers, and graduate students) in working with data, and identified services within the following campus units: library, IT department/research computing, independent research centers and facilities, academic departments, medical school, business school, and other professional schools. We also recorded whether the institution offered local high performance computing facilities. For detailed definitions, exclusions, and data collection procedures, please see the report referenced above.

Abstract copyright UK Data Service and data collection copyright owner.

Scotland 2 - fieldwork completed during week of August 23-31, 1974. A quota sample of 976 individuals were interviewed in 36 constituencies.
Main Topics:

Demographics; party preferences; party affiliation and voting intention; the Scottish National Party; attitudes to the governance of Scotland; Scottish independence; Scottish Assembly/Parliament.

This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories.Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a self-deposit with curation service. The deposit workflow is as follows:Create your repository account.If you are a first-time depositor, complete the U of G Research Data Repositories Dataset Deposit Intake Form.Activate your Data Repositories account by logging in with your U of G central login account.Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories.Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away.Prepare your dataset.Assemble your dataset following the Dataset Deposit Guidelines. Use the README file template to capture data documentation.Create a draft dataset record.Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide.Submit your draft dataset for review.Dataset review.Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Guidelines using a standard curation workflow.The curator will collaborate with you to enhance the dataset.Public release.Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.