This dataset contains statistically weighted estimates of initial education levels, highest education levels, and initial education locations for 43 key health workforce professions actively licensed in California as of July 1st, 2023. These metrics can be compared by workforce category, license type, time since license issue date (in years), race & ethnicity group, assigned sex at birth, and CHIS region.

National Science Foundation's Access is a program that provides advanced computing systems and services to researchers and educators, completely free of charge. Established to help researchers utilize the nation's advanced computing systems and services, Access is designed to meet the increasing demand for cyberinfrastructure in the scientific community. nnAccess offers a range of resources, from advanced computational resources to data management and analytics, all accessible through a single interface. With a focus on collaboration and community-driven innovation, Access is dedicated to empowering researchers to tackle complex scientific problems and drive discovery.

Developing trends in educational research for the 21st century education None

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

Quality criteria, analysed data sets, and criteria assessment results of a study in Jul 2019 and Mar 2021. The study was part of a Master Thesis in Information Science (Autor: Gabriel Schneider, Titel: Qualität von Forschungsdaten der Bildungsforschung in offenen Repositorien).

This data set contains an expanded data set and a sub-set (14) of the original 16 quality criteria. Original criteria were translated into English for a presentation at LIDA 21.

Quality criteria were developed based on expertise from the Research Data Centre for Education and the FAIR principles.

In the first study 2019, 29 data sets from Zenodo (search=keyword:Education and type:dataset) were analysed according to the criteria. 20 data sets were excluded due to access restrictions or topic (non educational research).

In 2021, 11 data sets uploaded at Zenodo 2021 were analysed according to the criteria. As search function at Zenodo changed, the search was adapted ((search=keyword:*Education OR keyword:*education) AND type:dataset AND accessright:open). Some data sets were excluded due to topic (non educational research), year published (2020 excluded), language barrieres or insufficient avalaible data.

The files include:

- Criteria: The criteria and assessment points applied

- Dataset: The search terms for the retrieved data sets, and exclusion criteria

- Results: The assessment points given for each criteria to each data set (without further details on decision with regard to specifics of data sets)

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (a) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (b) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword “data science education.” While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, substantial knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions, and reproducibility. Certain disciplines are less visible. We recommend that we should (a) cherish data science as an interdisciplinary field; (b) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (c) prioritize investments in empirical studies.

In the arts and humanities, digital data production is still expensive, challenging and time-consuming. We all know this, and yet the results of these processes often in the end can’t be reused by other researchers, meaning that we reinvent (or redigitise) the wheel far too often. This resource is aimed at giving practical advice for arts and humanities scholars who are willing to take their first steps in research data management but don't know where to begin. Our approach to data management views it as a reflective process that exposes and tweaks existing behaviours, rather than one that introduces specific tools. It is intended to encourage awareness of one’s own processes and mindfulness about how they could be more open and how and how small changes across three points in your research workflow can make big differences.

The USAID Opportunities for Equitable Access to Quality Basic Education (OPEQ) activity was a school-based intervention implemented by the International Rescue Committee (IRC) in partnership with the DRC’s Ministry of Education and focusing on early grade reading and math skills. The IRC integrated into OPEQ its social-emotional skills building curriculum which has resulted from the IRC’s education research and experience in conflict and crisis-affected areas. The OPEQ activity had four components: (1) informing national level in-service teacher-training policy and systems, (2) community mobilization and engagement, (3) alternative education and vocational training for out-of-school youth, and (4) Learning in a Healing Classroom (LHC), an in-service teacher professional development and integrated curricular program. The impact evaluation of OPEQ’s LHC activity employed a 3-year cluster-randomized trial, measuring the impact of LHC in treatment schools to wait-list control schools. The intervention was assigned to two cohorts defined by geographic location and timing of implementation. The Katanga 4 cohort had schools that received one year and two years of LHC located in Katanga province. The Kivu+ cohort had schools that received one year of the LHC intervention located in South Kivu and some parts of Katanga province. At each school in the sample, field researchers surveyed 81 students randomly selected from the student enrollment register. For the baseline and midline phases, students in the 2nd, 3rd, and 4th grades were tested. For the endline phase, students in 3rd, 4th, and 5th grades were tested.

The Children's Environmental Health Initiative (CEHI) at Rice University provided access to blood lead data from the North Carolina Childhood Lead Poisoning Prevention Program surveillance registry; data on end-of-grade standardized achievement tests in reading and mathematics from the North Carolina Education Research Data Center (NCERDC); and birth certificate data from the North Carolina Department of Health and Human Services. Test score, blood lead, and birth certificate data were linked using a common child identifier created by CEHI for matching purposes. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data was made available by the Children's Environmental Health Initiative (CEHI) at Rice University. Contact Claire Osgood (ceo1@rice.edu), CEHI's Data Manager, to learn how the data can be accessed. Format: The Children's Environmental Health Initiative (CEHI) at Rice University provided access to blood lead data from the North Carolina Childhood Lead Poisoning Prevention Program surveillance registry; data on end-of-grade standardized achievement tests in reading and mathematics from the North Carolina Education Research Data Center (NCERDC); and birth certificate data from the North Carolina Department of Health and Human Services. Test score, blood lead, and birth certificate data were linked using a common child identifier created by CEHI for matching purposes. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

LearnPlatform is a unique technology platform in the K-12 market providing the only broadly interoperable platform to the breadth of edtech solutions in the US K12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform has emerged as an important and singular resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a unique and needed source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact.In this dataset we are sharing educational technology usage across the 8,000+ tools used in the education field in 2020. We make this dataset available to public so that educators, district leaders, researchers, institutions, policy-makers or anyone interested to learn about digital learning in 2020, can use this dataset to understand student engagement with core learning activities during the COVID-19 pandemic. Some example research questions that this dataset can help stakeholders answer: What is the picture of digital connectivity and engagement in 2020?What is the effect of the COVID-19 pandemic on online and distance learning, and how might this evolve in the future?How does student engagement with different types of education technology change over the course of the pandemic?How does student engagement with online learning platforms relate to different geography? Demographic context (e.g., race/ethnicity, ESL, learning disability)? Learning context? Socioeconomic status?Do certain state interventions, practices or policies (e.g., stimulus, reopening, eviction moratorium) correlate with increases or decreases in online engagement?

Financial overview and grant giving statistics of Research Education & Access for Community Health

Research project

The project “Educational Success and Social Participation of Socially and Educationally Disadvantaged Students with Migration Background in Extended Education” (abbr. in German: GeLeGanz) was funded by the German Federal Ministry of Education and Research (BMBF) under the funding code 01JB211A-C from 2021 to 2025.

Traditionally, the German education system is organized as a “half-day”-system; instruction usually takes place in the morning. Many stakeholders see the conversion from half-day to all-day schooling as a way of overcoming the challenges facing the system, including those posed by immigration. High expectations are attached to the expansion of all-day schooling, in particular the strengthening of the educational success and social participation of socially and educationally disadvantaged students with a migration background. As yet however, these goals have not been sufficiently achieved in Germany. Education systems in other countries have established comparable offerings of high quality that appear to be effective. The GeLeGanz project aims to use findings and knowledge from other countries to better exploit the potential of all-day schools in Germany, particularly at the primary school level. The primary focus of the GeLeGanz project is on the potential of all-day primary schools to improve the educational opportunities of socially and educationally disadvantaged students, in particular those who live in a migrant family.

GeLeGanz is a collaborative project, carried out by three partners.

Freie Universität Berlin (FU):

• Prof. Dr. Marianne Schüpbach (Coordinator of the collaborative project, Co-head of the subproject FU Berlin): marianne.schuepbach@fu-berlin.de
• Dr. Nanine Lilla (Co-head of the subproject FU Berlin): nanine.lilla@fu-berlin.de
• Dr. Haiqin Ning (Researcher, Operational coordinator of the collaborative project): haiqin.ning@fu-berlin.de
• Jule Schmidt (Researcher): jule.swaantje.schmidt@fu-berlin.de

German Children and Youth Foundation (DKJS):

• Anna-Margarete Davis (Co-head of the subproject DKJS): anna-margarete.davis@dkjs.de
• Nicola Andresen (Co-Lead of the subproject DKJS and school counselling): nicola.andresen@dkjs.de

University of Hamburg (UHH):

• Prof. Dr. Dr. h.c. mult. Ingrid Gogolin (head of the subproject UHH): gogolin@uni-hamburg.de
• Luise Krejcik (Researcher): luise.krejcik@uni-hamburg.de

To achieve the objectives, the project was divided into the following phases:

Phase I: Expert interviews with researchers from the German and international research context on their perception of quality features and conditions for the successful design of all-day schools.

Phase II: The experts were interviewed again to evaluate and further specify the results with regard to the target group. For this, they were provided with a summary of the statements made by researchers from the German and international research context in Phase I.

Phase III: Focus group interviews with various practice-oriented actors from the German all-day school context, based on the results of expert interviews, to gain information and assessments related to the implementation of measures that might improve all-day schooling in Germany.

Phase IV: Based on the insights gained in the first three parts of the project, materials and concepts should be developed together with practice partner DKJS and transfer partners.

Project website: https://www.ewi-psy.fu-berlin.de/en/v/geleganz/index.html

Data set in UHH

The present data set comprises 30 expert interviews with 15 researchers from the German education research community, which were collected as part of the GeLeGanz project in phase I and II.

Experts: 15 researchers were interviewed twice (1x in phase I and 1x in phase II of the project). All were experts with relevant research experience, but different perspectives on the project’s guiding questions: all-day schools, informal and nonformal education, cultural and language diversity, social inequality and school development. The interview partners were identified via a review of empirical research on conditions of educational success of socially disadvantaged children with a migrant background and the potential advantages of all-day schools.

Interview procedure & topics: A sequential approach was chosen for conducting the interviews: In Phase I, interviewees were asked for

1. research-based assessments of features of high-quality all-day schools, especially for the support of socially disadvantaged children with a migration history,
2. factors that promote or hinder participation in all-day schools’ offers,
3. assessments of the current all-day school landscape in Germany.

In Phase II, the experts were interviewed again. They were provided with a summary of the statements made by the German and international experts in interviews of phase I. Experts were invited to prioritize the mentioned quality features and the potential for adaptation and implementation in the German context.

A semi-structured, problem-centred approach was used to conduct the interviews (Witzel, 2000). The guidelines included narrative-generating impulse questions, follow-up questions to promote understanding and narrative generation, and ad hoc questions on the topics discussed. The interviews were conducted in German by two trained interviewers (online or analogous). All interviews were recorded based on informed consent.

Period of the survey: The interviews were conducted from March to December 2022.

Transcription & anonymization: The transcripts were initially computer-generated, then completely revised manually according to established transcription and anonymization rules (Rädiker and Kuckartz, 2019, p. 44f).

Contents of the data set UHH:

• Collected storage of the 30 interview transcripts in a single MAXQDA project (version 2022, MX22-file)
• Individual file of the 30 interview transcripts in Excel format (xls) and html format
• Anonymization and transcription rules, data naming scheme (pdf)

Note: The dataset is stored in the ZFMD repository of the University of Hamburg in both an open-access (DOI 10.25592/uhhfdm.14815) and a restricted-access version (DOI 10.25592/uhhfdm.14771). Both datasets are available from January 1, 2026. In the open access dataset, research-related data such as research projects and studies of the respondents are anonymized in addition to personal and school-related data. In the restricted access dataset, only the respondents' personal and school-related data are anonymized.

References:

Rädiker, S., & Kuckartz, U. (2019). Analyse qualitativer Daten mit MAXQDA: Text, Audio und Video. Springer Fachmedien.

Witzel, A. (2000). Das problemzentrierte Interview [25 Absätze]. Forum: Qualitative Social Research, 1(1), Article 22. http://nbnresolving.de/urn:nbn:de:0114-fqs0001228

OpenCon 2015 Application Open Data

The purpose of this document is to accompany the public release of data collected from OpenCon 2015 applications.Download & Technical Information The data can be downloaded in CSV format from GitHub here: https://github.com/RightToResearch/OpenCon-2015-Application-Data The file uses UTF8 encoding, comma as field delimiter, quotation marks as text delimiter, and no byte order mark.

License and Requests

This data is released to the public for free and open use under a CC0 1.0 license. We have a couple of requests for anyone who uses the data. First, we’d love it if you would let us know what you are doing with it, and share back anything you develop with the OpenCon community (#opencon / @open_con ). Second, it would also be great if you would include a link to the OpenCon 2015 website (www.opencon2015.org) wherever the data is used. You are not obligated to do any of this, but we’d appreciate it!

Data Fields

Unique ID

This is a unique ID assigned to each applicant. Numbers were assigned using a random number generator.

Timestamp

This was the timestamp recorded by google forms. Timestamps are in EDT (Eastern U.S. Daylight Time). Note that the application process officially began at 1:00pm EDT June 1 ended at 6:00am EDT on June 23. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. [a]

Gender

Mandatory. Choose one from list or fill-in other. Options provided: Male, Female, Other (fill in).

Country of Nationality

Mandatory. Choose one option from list.

Country of Residence

Mandatory. Choose one option from list.

What is your primary occupation?

Mandatory. Choose one from list or fill-in other. Options provided: Undergraduate student; Masters/professional student; PhD candidate; Faculty/teacher; Researcher (non-faculty); Librarian; Publisher; Professional advocate; Civil servant / government employee; Journalist; Doctor / medical professional; Lawyer; Other (fill in).

Select the option below that best describes your field of study or expertise

Mandatory. Choose one option from list.

What is your primary area of interest within OpenCon’s program areas?

Mandatory. Choose one option from list. Note: for the first approximately 24 hours the options were listed in this order: Open Access, Open Education, Open Data. After that point, we set the form to randomize the order, and noticed an immediate shift in the distribution of responses.

Are you currently engaged in activities to advance Open Access, Open Education, and/or Open Data?

Mandatory. Choose one option from list.

Are you planning to participate in any of the following events this year?

Optional. Choose all that apply from list. Multiple selections separated by semi-colon.

Do you have any of the following skills or interests?

Mandatory. Choose all that apply from list or fill-in other. Multiple selections separated by semi-colon. Options provided: Coding; Website Management / Design; Graphic Design; Video Editing; Community / Grassroots Organizing; Social Media Campaigns; Fundraising; Communications and Media; Blogging; Advocacy and Policy; Event Logistics; Volunteer Management; Research about OpenCon's Issue Areas; Other (fill-in).

Data Collection & Cleaning

This data consists of information collected from people who applied to attend OpenCon 2015. In the application form, questions that would be released as Open Data were marked with a caret (^) and applicants were asked to acknowledge before submitting the form that they understood that their responses to these questions would be released as such. The questions we released were selected to avoid any potentially sensitive personal information, and to minimize the chances that any individual applicant can be positively identified. Applications were formally collected during a 22 day period beginning on June 1, 2015 at 13:00 EDT and ending on June 23 at 06:00 EDT. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. Applications were collected using a Google Form embedded at http://www.opencon2015.org/attend, and the shortened bit.ly link http://bit.ly/AppsAreOpen was promoted through social media. The primary work we did to clean the data focused on identifying and eliminating duplicates. We removed all duplicate applications that had matching e-mail addresses and first and last names. We also identified a handful of other duplicates that used different e-mail addresses but were otherwise identical. In cases where duplicate applications contained any different information, we kept the information from the version with the most recent timestamp. We made a few minor adjustments in the country field for cases where the entry was obviously an error (for example, electing a country listed alphabetically above or below the one indicated elsewhere in the application). We also removed one potentially offensive comment (which did not contain an answer to the question) from the Gender field and replaced it with “Other.”

About OpenCon

OpenCon 2015 is the student and early career academic professional conference on Open Access, Open Education, and Open Data and will be held on November 14-16, 2015 in Brussels, Belgium. It is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. The meeting will convene students and early career academic professionals from around the world and serve as a powerful catalyst for projects led by the next generation to advance OpenCon's three focus areas—Open Access, Open Education, and Open Data. A unique aspect of OpenCon is that attendance at the conference is by application only, and the majority of participants who apply are awarded travel scholarships to attend. This model creates a unique conference environment where the most dedicated and impactful advocates can attend, regardless of where in the world they live or their access to travel funding. The purpose of the application process is to conduct these selections fairly. This year we were overwhelmed by the quantity and quality of applications received, and we hope that by sharing this data, we can better understand the OpenCon community and the state of student and early career participation in the Open Access, Open Education, and Open Data movements.

Questions

For inquires about the OpenCon 2015 Application data, please contact Nicole Allen at nicole@sparc.arl.org.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).

Release of Sweeps 1 to 4 to Long Format (Summer 2020)
To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

Latest edition information:
For the second edition (February 2017), the data file has been updated to correct discrepancies found in the first edition.

This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html.

The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available:

2019 EUA Open Science and Access Survey

Database in the following formats: .xlsx (Microsoft Excel)

Survey Codebook: includes information on all the variables and their coding.

The global market for academic research databases is experiencing robust growth, projected to reach $388.2 million in 2025. While the exact Compound Annual Growth Rate (CAGR) is not provided, considering the ongoing digitalization of research and education, a conservative estimate would place the CAGR in the range of 7-9% for the forecast period (2025-2033). This growth is fueled by several key drivers. The increasing reliance on digital resources by students, teachers, and researchers across all academic disciplines is a significant factor. Furthermore, the expanding volume of scholarly publications and the need for efficient access and management of research data are propelling market expansion. The rising adoption of cloud-based solutions and the development of sophisticated search and analytical tools within these databases are also contributing to this growth trajectory. The market segmentation highlights the diverse user base, with students, teachers, and experts representing major segments, each with varying needs and subscription models (charge-based or free access). The competitive landscape is characterized by established players like Scopus, Web of Science, and PubMed, alongside other significant contributors like ERIC, ProQuest, and IEEE Xplore, indicating a market with both established dominance and emerging players vying for market share. Geographic distribution shows a strong presence across North America and Europe, but with significant growth potential in Asia-Pacific regions. The market's future trajectory will likely be shaped by several trends. The increasing integration of artificial intelligence (AI) for enhanced search and data analysis capabilities will be a major factor. The ongoing development of open-access initiatives and the expansion of free databases will influence market dynamics, potentially impacting the revenue streams of subscription-based services. However, challenges such as data security concerns, the need for continuous content updates, and the varying levels of digital literacy across different user groups may act as restraints on market growth. Nevertheless, the overall outlook for the academic research database market remains positive, driven by the continued expansion of scholarly research and the growing demand for efficient and reliable access to research information globally.

The eighth cycle of the Ithaka S+R Faculty Survey queried a random sample of higher education faculty members in the United States to learn about their attitudes and practices related to their research and teaching. Respondents were asked about resource discovery and access; research topics and practices; research dissemination, including open access, data management, and preservation; instruction and perceptions of student research skills; the role and value of the academic library; and open-educational resources. Demographic variables include the respondent's age, gender, primary academic field, title or role, institution's Carnegie classification, how many years the respondent has worked at their current college or university, how many years the respondent has worked in their field, what format the courses they are currently teaching (if any) are in (synchronous, asynchronous, or a mix of both) and whether the respondent primarily identifies as a researcher, teacher, or somewhere in between.

Academic Strive is a leading online publishing group that empowers researchers with ample scientific ideas and research information. The organization is an international online publishing house that publishes Open Access Journals in various research fields of science and technology. Academic Strive Journals acts as a connecting link between researchers and readers across the scientific community, providing a platform where anyone can view, share, and download scientific research information.

The organization's vision is to access the emerging literature and promote knowledge transfer among research communities. With a vast library of categorized topics, each paper is peer-reviewed and approved by editors. Academic Strive aims to build an open scientific platform where everybody gets an equal opportunity to seek, generate, and share knowledge, empowering researchers and scholars in their daily work.

The uploaded material contained the slides of the Course "Scholarly Communication in the Digital Age. An introduction to Open Science practice" For PhD Students held at the Scuola Sant'Anna of Pisa, February / March 2022. The course is organized in 4 modules. Module 1: Introduction and motivation - What is Open Science? - Research assessment and Open Science Module 2: The Open Access publishing paradigm - Open Access publishing - Open Science in Europe (Horizon Europe, EOSC, Research Infrastructures) Module 3: Research Data Management - Research Data Management - FAIRness - Data Management Planning Module 4: Open Science workflows - OS workflows - FAIRification in practice - How to think about your CV

Graphics from the Why Open Research project to mark Open Access Week 2017. The theme this year is "Open in order to...", which prompts us to answer the question: What can we accomplish by sharing our work as educators and researchers?All images are openly licensed for reuse. File format for all images is png. Master files are also included in Keynote and Powerpoint formats so that modifications can be made as needed. Please note that some formatting may be lost in the conversion to ppt. For correct formatting, please refer to the png images.