The purpose of this study was to examine the crime of identity theft from the offenders' perspectives. The study employed a purposive sampling strategy. Researchers identified potential interview subjects by examining newspapers (using Lexis-Nexis), legal documents (using Lexis-Nexis and Westlaw), and United States Attorneys' Web sites for individuals charged with, indicted, and/or sentenced to prison for identity theft. Once this list was generated, researchers used the Federal Bureau of Prisons (BOP) Inmate Locator to determine if the individuals were currently housed in federal facilities. Researchers visited the facilities that housed the largest number of inmates on the list in each of the six regions in the United States as defined by the BOP (Western, North Central, South Central, North Eastern, Mid-Atlantic, and South Eastern) and solicited the inmates housed in these prisons. A total of 14 correctional facilities were visited and 65 individuals incarcerated for identity theft or identity theft related crimes were interviewed between March 2006 and February 2007. Researchers used semi-structured interviews to explore the offenders' decision-making processes. When possible, interviews were audio recorded and then transcribed verbatim. Part 1 (Quantitative Data) includes the demographic variables age, race, gender, number of children, highest level of education, and socioeconomic class while growing up. Other variables include prior arrests or convictions and offense type, prior drug use and if drug use contributed to identity theft, if employment facilitated identity theft, if they went to trial or plead to charges, and sentence length. Part 2 (Qualitative Data), includes demographic questions such as family situation while growing up, highest level of education, marital status, number of children, and employment status while committing identity theft crimes. Subjects were asked about prior criminal activity and drug use. Questions specific to identity theft include the age at which the person became involved in identity theft, how many identities he or she had stolen, if they had worked with other people to steal identities, why they had become involved in identity theft, the skills necessary to steal identities, and the perceived risks involved in identity theft.

United States Number of Researchers: Total data was reported at 1,889,780.000 Person in 2021. This records an increase from the previous number of 1,823,522.000 Person for 2020. United States Number of Researchers: Total data is updated yearly, averaging 1,856,651.000 Person from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 1,889,780.000 Person in 2021 and a record low of 1,823,522.000 Person in 2020. United States Number of Researchers: Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

This study sought to provide in-depth insight about the complex interaction of factors influencing motivations for sharing and re-using open research data within a single discipline, namely astrophysics. We identified the following research questions. 1. What discipline-specific characteristics influence motivation for sharing and re-using open research data? 2. What factors influence researcher’s motivations to openly share their data? 3. What factors influence researcher’s motivations to re-use open research data shared by others? 4. How can researchers in disciplines with low rates of open data sharing and re-use be encouraged to share and re-use more? These research questions are addressed through a case study consisting of nine in-depth interviews with astrophysics researchers and through observations of researchers in this discipline. With permission of the interviewees, all interviews were recorded, and these recordings were transcribed. The checked transcripts were imported into the ATLAS.ti software. ATLAS.ti software was used for open, axial, and selective coding.

The goal of this study was to conduct a national empirical assessment of post-release employment and recidivism effects based on legislative intent for inmates participating in Prison Industries Enhancement Certification Program (PIECP) as compared to participants in traditional industries (TI) and those involved in other than work (OTW) activities. The research design for this study was a quasi-experimental design using matched samples. The inmates were matched using six criteria. Exact matches were made on race, gender, crime type, and category matches on age, time served, and number of disciplinary reports. A cluster sampling strategy was used for site selection. This strategy resulted in a selection of five states which were not identified in the study. The researchers then collected data on 6,464 individuals by completing record reviews of outcomes for the 3 matched samples, each of approximately 2,200 inmates released from 46 prisons across 5 PIECP states between January 1, 1996, and June 30, 2001. Variables include demographic information, time incarcerated, number of disciplinary reports, crime type, number of major disciplinary reports reviewed, group type, number of quarters from release to employment, censored variables, number of quarters from employed to job loss, time from release variables, number of possible follow-up quarters, proportion of follow-up time worked, wage variables, number of quarters worked variables, no work ever, and cluster number of case.

These data are part of NACJD's Fast Track Release and are distributed as they are received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study is to examine the prevalence of police practitioner-research partnerships in the United States and examine the factors that prevent or facilitate development and sustainability of these partnerships. This study used a mixed method approach to examine the relationship between law enforcement in the United States and researchers. A nationally-representative sample of law enforcement agencies were randomly selected and given a survey in order to capture the prevalence of police practitioner-researcher partnerships and associated information. Then, representatives from 89 separate partnerships were interviewed, which were identified through the national survey. The primary purpose of these interviews was to gain insight into the barriers and facilitators of police and practitioner relationships as well as the benefits of this partnering. Lastly four case studies were conducted on model partnerships that were identified during interviews with practitioners and researchers.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This project utilized three strategies to investigate the impact of incarceration on families. First, a statewide integrated data system was used to examine impacts of incarceration in a novel way, using administrative data from corrections, juvenile justice, mental health, social services, substance use services, healthcare, and education. Second, researchers linked multi-agency data to address specific research questions regarding impact of incarceration on families, including impact of incarceration on family physical and mental health, children's involvement with the child welfare and juvenile justice systems, family economic status, and school performance. Third, researchers conducted focus groups and family interviews with 77 inmates and 21 inmate family members sampled from three correctional facilities. Researchers identified qualitative themes regarding impact of incarceration in the lives of inmates and their families.Only data from the focus groups is included in this collection. The collection includes two SPSS data files: "Inmate_Demographic_Data.sav" with 15 variables and 77 cases and "Family_Demographic_Data.sav" with 19 variables and 21 cases. The actual focus group interviews with inmates and their family members are not available as part of this collection at this time. Administrative data from the South Carolina Revenue and Fiscal Affairs Office was not made available for archiving. Users interested in obtaining these data should consult the accompanying documentation.

This is the dataset underlying the research article, “What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption”. It provides main information concerning the articles identified through the systematic literature review applied in this study, as well as detailed information concerning the 32 studies selected for the literature review. Furthermore, the file provides information derived from the description and analysis of the selected studies. More information can be found in the README file.

Researcher mobility is an integral part of the way research is conducted and of a researcher’s career. Its effects on collaboration networks, research impact and knowledge flows drive countries and institutions to quantify and understand this activity. The purpose of this study is to test a new researcher mobility model which was developed and prototyped as a customisable research tool to provide a unified perspective on mobility at macro (national), meso (institutional) and micro (individual) levels. The approach includes multidimensional perspectives, including temporal, geographical, sectoral, directional mobility, that could be used for benchmarking and trend analyses. The model quantifies research mobility volumes and qualifies the mobility flow additional researcher characteristics and productivity indicators. We tested the tool among Sweden’s higher education sector, observing researcher mobility patterns between 1992–2021. Results show a high degree of variability in researcher mobility patterns across institutions, especially when considered by career age. Larger higher education institutions in Sweden tend to see a high level of inter-university mobility: most of the Outflow researchers have international mobility and were affiliated with organisations from diverse sectors. Smaller universities are more adapted to attract early- and retain late-career researchers. One university was identified as an incubator for early-career researchers that go on to high levels of mobility. Another university achieved higher mobility rates by facilitating short-term mobility abroad. The study highlighted a shift in the countries of destination for the Inflow early-career researchers: fewer were affiliated with USA, UK or Japan, while other countries became more prominent (China, Germany, Netherlands, Spain) and new destinations emerged (Brazil, India, Iran). The study emphasized that visiting researchers are consistently more productive, and their research impact is generally higher. With the help of our advanced model, we present a detailed picture of mobility in Sweden and demonstrate the power of this customisable tool.

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

United States US: Total Researchers: Full-Time Equivalent data was reported at 1,639,258.000 FTE in 2021. This records an increase from the previous number of 1,513,964.000 FTE for 2020. United States US: Total Researchers: Full-Time Equivalent data is updated yearly, averaging 998,340.036 FTE from Dec 1981 (Median) to 2021, with 41 observations. The data reached an all-time high of 1,639,258.000 FTE in 2021 and a record low of 531,938.478 FTE in 1981. United States US: Total Researchers: Full-Time Equivalent data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

The following anonymised dataset is raw data from a qualitative study that focused on embedded researcher reflections. The data was collected between May 2024 and August 2024. The researchers were embedded within a local authority pilot which included a complex multi-system physical activity intervention in the North West of England. The researchers reflected upon the experience of being an embedded researcher. The data was used to prepare an academic paper to PLOS-ONE and has been accepted for publication in April 2025. Abstract: Physical inactivity remains a substantial public health concern, with complex socioenvironmental factors contributing to increasing inactivity. Whole systems approaches to physical activity seek to address these complexities by promoting multi-component, place-based interventions. This study reflects on the experiences of three embedded researchers working within a whole systems approach initiative aimed at reducing physical inactivity in the United Kingdom. Researchers were embedded within a local authority and affiliated to a university whilst responsible for evaluating the effectiveness and efficacy of the whole systems approach initiative. Using a reflective journaling method, followed by inductive thematic analysis, the findings identified key challenges and enablers to evaluating the initiative. Key challenges included the perceived value of research and evaluation within the local authority, a lack of capacity to conduct evaluative activity, and the presence of confirmation and reporting bias within the wider delivery team. Key enablers included relationship-building, skill development, and protected time for evaluation and research activity. The findings suggest that institutions supporting embedded researchers should establish regular contact with the local authority, help to establish realistic expectations, and support researchers to overcome emerging challenges. Recommendations for researchers include developing robust relationships, setting out clear expectations, and ensure they have protected time at key points during the evaluation. Through these recommendations, researchers may be better prepared to overcome implementation challenges and improve the efficiency of the evaluation process.

FR: Number of Researchers: Female data was reported at 139,651.000 Person in 2021. This records an increase from the previous number of 135,524.000 Person for 2020. FR: Number of Researchers: Female data is updated yearly, averaging 80,989.351 Person from Dec 2000 (Median) to 2021, with 20 observations. The data reached an all-time high of 139,651.000 Person in 2021 and a record low of 58,124.000 Person in 2000. FR: Number of Researchers: Female data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s France – Table FR.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

In France, from 2014 onwards, the R&D personnel in the university hospitals is better identified, introducing to a break in series in the higher education sector; moreover, from that year, university hospitals collect R&D personnel data by gender whereas these figures were previously estimated. The National Centre for Scientific Research (CNRS) is included in the Higher Education sector, whereas in other countries such as Italy for example, this type of organisation is classified in the Government sector. This affects comparisons of the breakdown of R&D efforts by sector of performance.

The methodology of the public administrations survey was changed in 2010: the method for measuring the resources devoted to R&D in ministries and some public organisations has been modified, leading to a better identification of their financing activities. The impact is notably a 900 million fall in GOVERD and a 3 200 drop in FTE personnel.

From 2004 onwards, a new methodology was introduced to correct for some double-counting of funds for universities. In 2007, the sampling method in the BE sector was modified and the 2004 data revised according to the new methodology.

Beginning with the 2006 survey, in order to better take into account SMEs, there is no longer a cut-off point in the business enterprise sector of one Full-time-equivalent on R&D for an enterprise to be included in the survey population.

From 2001, coverage of the BE sector was expanded. Data communicated by the Ministry of Defence were also extended to cover research that was not considered R&D in earlier years. This also affected GBARD data.

In 2000, several methodological changes which improved the quality of the public sector data resulted in a break in series for that year: social charges and civil pensions are better captured in universities' research expenses; modification of responses from some institutes to better harmonise with the corresponding multi-annual programme; and implementation of a redesigned questionnaire. National sources estimate that the previous method would have produced a 1.6% increase in GERD, where the current method resulted in 4%.

Due to changes in the methods used to evaluate domestic expenditure on defence, the results of the 1998 surveys revealed significant modifications requiring new estimates for 1997. This break in series relates also to the GBARD data.

In 1997, the method used to measure R&D personnel in administrations has changed.

Between 1991 and 1992 France Télécom and GIAT Industries were transferred from the Government to the Business Enterprise sector following a change in their legal status.

Before 2016, part of R&D budgets cannot be allocated by NABS socio-economic objective. In 2006 and 2007, following the implementation of the Constitutional Bylaw on Budget Acts (LOLF act: 'loi organique relative aux lois de finances'), some departments are no longer recorded in the GBARD data. Consequently, total GBARD is underestimated for both years.

Abstract

The outcome of well-performed clinical research is essential for evidence-based patient management during pandemics. However, conducting clinical research amidst a pandemic requires researchers to balance clinical and research demands. We seek to understand the values, experiences, and beliefs of physicians working at the onset of the COVID-19 pandemic in order to inform clinical research planning. We aim to understand whether pandemic settings affect physician comfort with research practices, and how physician experiences shape their understanding of research in a pandemic setting.

A survey tool was adapted to evaluate familiarity and comfort with research during a pandemic. A cross-sectional, online questionnaire was distributed across Canadian research networks early in the COVID-19 outbreak. The survey was administered between March 11^th and 17^th, 2020, during a time of local transmission but prior to the surge of cases. We aimed to recruit into the survey physicians in infectious disease and critical care research networks across Canada.

Of the 133 physician respondents, 131 (98%) considered it important to conduct clinical research during the COVID-19 pandemic. Respondents were more accepting of adaptations to the research process during a pandemic compared to in a non-pandemic setting, including conducting research with deferred consent (χ² = 8.941, 95% CI: -0.264, -0.085, p = 0.003), using non-identifiable observational data with a waiver of consent with a median score of 97 out of 100 (IQR: 79.25–100) vs median 87 out of 100 (IQR: 63–79) (95% CI: -12.43, 0.054, p = 0.052). The majority felt that research quality is not compromised during pandemics.

Physicians consider it important to conduct research during a pandemic, highlighting the need to expedite research activities in pandemic settings. Respondents were more accepting of adaptations to the research process for research conducted during a pandemic, compared to that conducted in its absence of a pandemic.

AbstractWorking groups are recognized as a highly effective method for synthesizing science. It is less clear if participating in working groups benefits individual researchers, or if benefits differ between men and women. This is a critical question, for the working group method is not sustainable if the benefit to science comes at a cost to academic careers or gender equity. Here, we analyze the publications of Canadian university faculty specialized in ecology and evolution (N=1244), a field that has embraced the working group method. Researchers were more likely to have participated in a working group as their academic age and prior H-index increased, but controlling for these factors there was no effect of gender. Using a longitudinal analysis, we find that researcher H-indices accrue 14% faster following their first working group publication, regardless of gender. Part of this acceleration may be the 3- to 5-fold higher citation rate of working group synthesis publications. In a survey (N=169), researchers also report indirect benefits of working groups, at similar rates for men and women. Working groups are therefore, good not just for science but also for scientists. Formalized mechanisms for collaborations such as working groups may also offset gender inequities in science. MethodsWe compiled information on 1,244 faculty members at Canadian universities who were funded by a NSERC Discovery grant (Evolution and Ecology subcommittee) between 1991 and 2019. This information included assumed binary gender from first names and institutional website use of pronouns and photographs (coded men, women); we acknowledge that we may have mis-assigned gender or failed to notice non-binary, transitional or fluid gender identities. We also collected information on the researcher’s year of PhD and all institutions they were affiliated with during their research career. This information was obtained from public curriculum vitae, institutional websites, personally-maintained researcher websites, academic networking platforms (LinkedIn, Research Gate), Google Scholar, and other public sources such as obituaries. For each researcher, we reconstructed their H-index through time using (1) a compiled list of their peer-reviewed publications and (2) the citations for each publication, for each calender year from the date of publication until 2019. We compiled their publications using a recursive procedure, which started by first downloading all publications for individuals with the researcher’s first initial and last name from Web of Science Core Collection (hereafter, WOS) starting from 5 years prior to their PhD until 2019, and then filtering this list by cross-referencing with known variants in authorship names for the researcher (from online curriculum vitae or Google Scholar profile) as well as their institutional affiliations, fuzzy matching of publication titles from their curriculum vitae or Google Scholar profile where possible, and recursive identification of previously unidentified affiliations to fine-tune the cross-referencing procedure. Once we had cleaned the publication record, we then calculated cumulative citations over years for each publication from WOS yearly citation counts as a precursor to calculating the H-index. We identified a potential pool of publications from working groups by (1) matching WOS titles with known working group publications funded by the 15 synthesis centers that comprise the International Synthesis Consortium, (2) by searching the funding and acknowledgment sections of publications for synthesis centre names or acronyms, or keywords commonly used to describe working groups (“working group”, “synthesis group”, “synthesis working group”, “synthesis committee”, “synthesis workshop”, “catalysis group”). All publications from steps 1 and 2 were then manually coded as primary research vs. synthesis research, and as working group method vs. non-working group method. We further categorized synthesis research publications into the following types: statistical synthesis (statistical analysis of previously published or archived data collected by multiple different researchers and/or studies), conceptual synthesis (qualitative review of the literature or proposal of new frameworks for scientific concepts or investigation), or mathematical synthesis (theoretical mathematical models or specific application of general models for the purpose of prediction). We scored non-working group publications using similar criteria. However, given the large number of publications involved, we changed methods to allow for programmatic approaches based on keywords indicative of the three types of synthesis science. This data is presented in aggregated and anonymized form as needed to prevent the identification of individuals. We conducted an online survey of current ecology and evolution faculty in Canada from July to September 2019, recruited by email and supplemented by in-person recruitment at the...

IntroductionHealth promotion research is marked by recognizing diverse forms of knowledge, the embeddedness of research practices in context, the relationship between researchers and stakeholders, and the articulation of knowledge production and sharing. Amid this epistemology, researchers’ understanding of their roles in specific projects and programs led by different stakeholders is essential. We used a global initiative to promote governance for health and wellbeing in five cities of different low-and middle-income countries as a case study to analyze senior-level researchers’ understanding of their role within the initiative.MethodsWe conducted a qualitative content analysis, supported by computer-assisted qualitative data analysis software, of verbatim interview transcripts from semi-structured qualitative interviews with the full sample of senior-level health promotion researchers (n = 5) who supported implementation of the initiative.ResultsWe identified three diverging types of local researchers’ roles understandings: (1) active deep involvement in collaborative arrangements, (2) balancing between active involvement and passively supporting, and (3) passively supporting the initiative. Researchers transcended sectoral boundaries to varying degrees and acted at the nexus between academic, practice, and policy communities.DiscussionOur proposed typology delineating the roles of senior-level health promotion researchers has the potential to stimulate reflexivity regarding role comprehension and underlying assumptions among all stakeholders before and during the implementation of ongoing and future urban health initiatives.

The aim of this thesis is to provide an understanding of, and solutions to, England and Wales (E&W) prison violence using a General Strain Theory (GST) framework. The GST framework is designed to be able to provide explanations for a variety of types of behaviours and criminal activities. A recent research paper by Blevins et al. (2010) outlined how to apply GST with three major prison theories that are often used to understand prisoner violent behaviours - the deprivation model, the importation model, and the coping model.

Since Blevins et al.’s (2010) article was published prison researchers have tested this theory’s understanding of prison strains and behaviour. However, these research studies have predominantly been conducted in United States (U.S.) prisons using a quantitative approach. This research offers a unique perspective by testing this theory qualitatively and by focusing on an E&W prison context. E&W prison strains will differ substantially from the strains highlighted in U.S. prison studies due to cultural and structural differences. It is therefore necessary for this research to highlight the specific strains that affect E&W prisoners’ violent behaviour.

This research comprised interviews with 41 recently released prisoners housed in 11 Approved Premises. Through analysing the interview data, I identified three systemic strains that are contributing to prison violence. These systemic strains are as follows: the strain of blocked desired costly goods; the strain of prison staff attitudes, disrespect, lack of support and misuse of power; and the strain of a strict regime. Each systemic strain produces and increases the magnitude of various stressors inside prisons. These stressors create pressure on some prisoners to react violently to attract attention, to defend themselves, to remove anticipated risks or to gain emotional relief. In order to reduce violence in E&W prisons these systemic strains need to be addressed.

For my PhD research I sought to provide an understanding of, and provide solutions to, prison violence in England and Wales using a General Strain Theory Framework. In order to understand how violence manifests itself, this research identified what strains are being produced by the prison environment that are linked to prisoner violent coping behaviour. This research comprised semi-structured interviews with 41 recently released prisoners housed in 11 Approved Premises. Approved Premises are probation institutions designed to house high-risk recently released prisoners. The Approved Premises selected were situated in a variety of towns across Northern England. The interviews took place within the Approved Premises in a quiet room provided by staff members. The participants were asked a range of questions that were pre-designed in the form of a topic guide in order to capture the various strains in Modern English and Welsh prisons that are potentially causing violent prisoner behaviour. Furthermore, as this research utilised a semi-structured interview, probing questions were asked depending on the answers that were given in the interview.

Overall, the interview collection process was successful. The data collection lasted less than 2 months. I was able to capture a wide range of insightful diverse data largely due to my designed topic guide. All the data was transcribed by the researcher. Once transcribed, the data was uploaded onto NVivo and was qualitatively analysed using thematic analysis. Once the sub-themes were created, I created by hand thematic maps in which I connected the sub-themes together in order to produce my main themes which I called systemic strains. After creating my main themes, I began writing up my three-chapter findings using the data-extracts to back up my arguments. I have now produced my 100,000-word thesis and have deposited it onto the WhiteRoseThesisOnline website.

As part of a bigger on-going mixed methods study exploring the perceptions and experiences of various stakeholders on the informed consent process for genetic/genomic research in Uganda qualitative data was collected and analyzed to identify genetic/genomic researchers’ perspectives on biological sample data sharing in collaborative research. The study was conducted at Makerere University College of Health Sciences (MakCHS), one of the nine constituent colleges at Makerere University in Uganda. All participants were researchers actively involved in genetic/genomic research in Uganda and affiliated to Makerere University College of Health Sciences (MakCHS). Participants were principal investigators of protocols involving host genomics and genetics research that were approved by Uganda National Council for Science and Technology (UNCST) for the period 2012-2017. UNCST provides regulatory oversight of all research activities in the country; and per local regulations, all protocols approved by accredited research ethics committees are submitted to UNCST for approval and registration. We searched archived research protocols approved by UNCST for the period 2012-2017. Only investigators based at MakCHS and its affiliate research institutes were eligible. A list of 23 investigators was generated and all were invited to participate but only 15 consented and participated in the study, of which three were H3Africa principal investigators. The number of researchers conducting genetics and genomic research at MakCHS is not known. However, it is important to note that there are several masters and PhD level scientists that are in training in genetic science and bioinformatics, mainly sponsored by the H3Africa initiative (H3Africa). Fifteen qualitative in-depth interviews were conducted between February to June 2019 focusing on knowledge, perceptions and experiences of genetics and genomics researchers on the storage and future use of biological materials for research. Twelve of the researchers were male. All participants were purposively selected as they were conducting genetic/genomic research at MakCHS. The interviews focused on 4 main domains for analysis: 1) opinion on the collection of BM for reuse; 2) opinion on the BM export/transfer and regulation of biobanking research; 3) challenges faced by local researchers in collaborative biobanking research; and 4) possible solutions to improve/realize outcomes of biological sample and associated data sharing. Interviews, lasting between 45-60 minutes. All interviews were conducted in English. Verified transcripts were imported into NVivo 12 software (QSR International Pty Ltd, 2014) to manage and organize the data. Data analysis was conducted iteratively throughout the study using a thematic approach. A team based approach of thematic analysis was employed.
Ethical approval was obtained from the Makerere University School of Biomedical Sciences Higher Degrees and Research Ethics Committee followed by clearance by Uganda National Council for Science and Technology. Written informed consent was obtained from all participants prior to interview. All recordings and transcripts were de-identified, assigned special codes and stored on a password-protected computer. No participant identifying information was published.

This is the dataset underlying the research article, “What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption”. It provides main information concerning the articles identified through the systematic literature review applied in this study, as well as detailed information concerning the 32 studies selected for the literature review. Furthermore, the file provides information derived from the description and analysis of the selected studies. More information can be found in the README file.