Transcripts of in-depth interviews and group discussions with managers, researchers, ethics committee members, field data collectors and community members on the issues around ethical data sharing in the context of research involving women and children in urban India. We interviewed researchers, managers, and research participants associated with a Mumbai non-governmental organization, as well as researchers from other organizations and members of ethics committees. We conducted 22 individual semi-structured interviews and involved 44 research participants in focus group discussions. We used framework analysis to examine ideas about data and data sharing in general; its potential benefits or harms, barriers, obligations, and governance; and the requirements for consent. Both researchers and participants were generally in favor of data sharing, although limited experience amplified their reservations.

It is increasingly recognized that effective and appropriate data sharing requires the development of models of good data sharing practice capable of taking seriously both the potential benefits to be gained and the importance of ensuring that the rights and interests of participants are respected and that risk of harms is minimized. Calls for the greater sharing of individual level data from biomedical and public health research are receiving support among researchers and research funders. Despite its potential importance, data sharing presents important ethical, social, and institutional challenges in low income settings. This dataset comprises qualitative research conducted in India, exploring the experiences of key research stakeholders and their views about what constitutes good data sharing practice.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, this introduces many challenges, especially when managing potentially sensitive clinical data. The aim of this 1 hr virtual workshop is to provide participants with foundational knowledge that supports planning for open data in future research projects. Specifically, participants will: 1. Gain an understanding of the new Tri-Agency Research Data Management policy and the analogous progress of the University of British Columbia's (UBC) Research Data Management (RDM) strategy and how they can be applied. 2. Gain an understanding of the ethical, privacy, and legal considerations of sharing data. 3. Learn practical skills for incorporating open data language into REB applications and consent form. Workshop Agenda: 1. "Becoming familiar with the new Tri-Agency Research Data Management Policy" - Speaker: Eugene Barsky, Research Data Librarian, UBC Library 2. "Ethics and Practical Considerations of Open Data Sharing." - Speaker: Brittney Schichter, Director, Research Integration & Innovation, Provincial Health Service Authority (PHSA) Research and Academic Services This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Eugene Barksy: Tri-Agency Research Data Management Policy presentation and accompanying PowerPoint slides. Brittney Schichter: Ethics and Practical Considerations of Open Data Sharing presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

The dataset presents the outcomes of a PhD study investigating how municipalities manage the ethical dilemmas arising from the competing interests of multiple stakeholders in governing the shared accommodation industry. Platform enterprises operating in SA have altered how people think about paying for a place to stay, whether for social housing, business or leisure purposes. Some of these changes have had mixed results, leaving municipalities to deal with ethical dilemmas from a management and governance perspective. The inquiry was conducted through a qualitative multiple case study method using the cities of Cape Town and eThekwini municipalities as units of analysis. Semi-structured interviews and observations were the primary techniques for collecting the data from 20 research participants drawn from both municipalities, as well as from external private and public sector and community organisations. The study used the purposeful, snowballing and opportunistic sampling techniques to maximize the opportunity to get more insights from the multiple research participants. Thematic analysis of the qualitative data from semi-structured interviews was used. Following Collis and Hussey (2021), the analysis of data commenced immediately during the transcription process of the interviews. Upon completion of the interviews, the qualitative data underwent content analysis, employing Otter.ai for transcription and identifying response patterns. The first transcriptions of the interviews were then cross-checked with memos and observation notes made by the researcher during the interview phases. Following the feedback, the transcribed interview data was coded and concepts were produced. These concepts were then merged to form categories. The categories and the interpretations of the interviews were triangulated using memos, observation notes, and documents obtained from the two municipalities and organisations such as Airbnb and Tourism Grading Council of South Africa. The researcher adopted the common ways of coding recommended by other qualitative researchers (Myers, 2019; Rashid et al., 2019; Yin, 2018). The adopted procedure involves following a four-step approach for interpreting the research material, viz: preparation, exploration, specification, and integration. The four-step technique provided a more organised and systematic method of interpretation, which proved useful in the presentation of the research data. Once the individual interviews were transcribed with rigorous analysis, the responses to both sets of research questions were extracted and organised to produce into two data summary tables. One data summary table recorded the research participants’ key responses to the primary research questions, separating the responses of the internal research participants (municipal employees) from the external research participants (stakeholders including businesses and community organisations). In the same manner, the second data summary table recorded the research participants’ key responses to the secondary research questions. These data summary tables included the research participants’ recommendations for improved governance for both municipalities. A separate consolidated data summary table was developed to capture the data of the research participants with a national footprint including their recommendations. The dataset include the customised "Interview questionnaire" that were used in interviewing the two categories of research participants in each municipaity; and a third “Interview Questionnaire” for the research participants with a national footprint.

This study provides a comprehensive overview of research ethics in science using an approach that combine bibliometric analysis and systematic review. The importance of ethical conduct in scientific research to maintain integrity, credibility, and societal relevance has been highlighted. The findings revealed a growing awareness of ethical issues, as evidenced by the development of numerous guidelines, codes of conduct, and oversight institutions. However, significant challenges persist, including the lack of standardized approaches for detecting misconduct, limited understanding of the factors contributing to unethical behavior, and unclear definitions of ethical violations. To address these issues, this study recommends promoting transparency and data sharing, enhancing education, and training programs, establishing robust mechanisms to identify and address misconduct, and encouraging collaborative research and open science practices. This study emphasizes the need for a collaborative approach to restore public confidence in science, protect its positive impact, and effectively address global challenges, while upholding the principles of social responsibility and justice. This comprehensive approach is crucial for maintaining research credibility, conserving resources, and safeguarding both the research participants and the public.

Transcripts of interviews and focus groups with researchers and community members on experiences of and views about data sharing. As the data sharing movement gains momentum, we wanted to understand attitudes and experiences of relevant stakeholders about what constitutes good data sharing practice. We conducted fifteen interviews and three focus groups discussions involving 25 participants and found that they generally saw data sharing as something positive. Data sharing was viewed as a means to contribute to scientific progress and lead to better quality analysis, better use of resources, greater accountability, and more outputs. However, there were also important reservations including potential harms to research participants, their communities, and the researchers themselves. Given these concerns, several areas for discussion were identified: data standardization, appropriate consent models, and governance.

It is increasingly recognized that effective and appropriate data sharing requires the development of models of good data sharing practice capable of taking seriously both the potential benefits to be gained and the importance of ensuring that the rights and interests of participants are respected and that risk of harms is minimized. Calls for the greater sharing of individual level data from biomedical and public health research are receiving support among researchers and research funders. Despite its potential importance, data sharing presents important ethical, social, and institutional challenges in low income settings. This data set comprises qualitative research conducted in India, Kenya, Thailand, South Africa and Vietnam, exploring the experiences of key research stakeholders and their views about what constitutes good data sharing practice.

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform, author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria}, booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)}, pages = {1--7}, title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior}, year = {2019} }

@inproceedings{SrbaMonantMedicalDataset, author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria}, booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)}, numpages = {11}, title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims}, year = {2022}, doi = {10.1145/3477495.3531726}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3477495.3531726}, }

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.

Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.

Reporting mistakes in the dataset The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

sources.csv

articles.csv

article_media.csv

article_authors.csv

discussion_posts.csv

discussion_post_authors.csv

fact_checking_articles.csv

fact_checking_article_media.csv

claims.csv

feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

category of annotation (annotation_category). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).

type of annotation (annotation_type_id). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.

method which created annotation (method_id). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.

its value (value). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.

entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation annotations).

The dataset provides specifically these entity annotations:

Source reliability (binary). Determines validity of source (website) at a binary scale with two options: reliable source and unreliable source.

Article veracity. Aggregated information about veracity from article-claim pairs.

The dataset provides specifically these relation annotations:

Fact-checking article to claim mapping. Determines mapping between fact-checking article and claim.

Claim presence. Determines presence of claim in article.

Claim stance. Determines stance of an article to a claim.

Annotations are contained in these CSV files (and corresponding REST API endpoints):

entity_annotations.csv

relation_annotations.csv

Note: Identification of human annotators authors (email provided in the annotation app) is anonymised.

New data set generated by ESRC funded study that set out to work with a collection of qualitative interviews exploring young people's (16-21) intimate and sexual life histories on 1988-90 in Manchester. New data generated includes oral history interviews with original research team and associated ephemera as well as documentation of a range of reanimating experiments undertaken with contemporary communities. The project connects feminist activism, youth work and social research within one city over a thirty year period.

In contemporary times archives are just a click away. There has been an extraordinary flourishing in personal and community archiving, using commercial and open access digital resources as a way of showing and telling about who we are. Emerging new contributor-audiences are offering transformed possibilities of a public and popular social science. Analogous shifts in academic practice have been initiated by funding bodies requiring that datasets are archived. This prescient move anticipated the digital revolution that would transform our ability to share and re-use data, assuring UK social scientists a leading role in debates around open archives and opportunities for data linkage and secondary analysis. Before 1996 the norm was that the documentation arising from qualitative social research was destroyed, lost - although some remained stored in attics and garages.

Our demonstrator project will secure and share an at-risk academic archive and bring it into dynamic conversation with a related community archive. We will harness the current extraordinary moment where lay and professional expertise are in dynamic equilibrium - with academia equipped to understand the protocols of long term preservation and community archives bringing new energy and imagination as to the value of data and what it might 'do' for and with us. At the same time, concerns about the ethics of visibility/ anonymity/ privacy, trust and the practicalities of sharing ownership, risk hindering the ability to realise these potentials. Through linking community archives with institutional repositories to facilitate an exchange of values, protocols and resources, we aim to develop the kinds of trust, imagination and inventive ethics for creative innovation to take place.

The substantive focus for our experiment is the question of teenage sexuality over a 30-year period, a question of public interest as well as academic contestation. We will work with two unique projects. The academic study is the influential ESRC-funded Women Risk and AIDS project (WRAP) conducted between 1988-90, involving 150 in-depth life history interviews with young women (16-21) in Manchester and London. The community archive is Manchester-based Feminist Webs a 'work space that acts as an archive and a resource for practitioners, volunteers and young women involved in youth and community work with young women'.

We will work with key stakeholders including archivists and museums, ethicists, youth workers, young people, data re-users, information scientists and data engineers, in order to do a number of things for the first time: return academic data to the community from which it was once extracted; to take careful risks in sharing documents without prior consent; enable distributed ownership using protocols to link institutional and community archives; re-enact research encounters.

Using drama methods with new generations of young women, practitioners and researchers, we will develop methods for public participation, collaborative analysis, to enact and re-perform the archive, creating new stories from our data, and new understandings of changes in the experience and portrayal of teenage sexualities over a complex thirty year period. We will create an open access online archive, including advice on practical and ethical guidelines platform, open access tools for data visualisation and analysis, that can be adapted and adopted by others, with the benefit of our learning on re-use, archiving and reanimating; including open educational resources materials targeted at schools as well as and trainee social scientists. Our aim is to inspire current and future researchers, academic and community-based, to archive and share their own data, to create linkage opportunities with community archives and academic datasets and popular research practices, which will allow us to better understand recent social change.

This project set out to save, digitise and archive a classic feminist social research data set (the Women Risk & AIDS Project 1988-90) and then to 'reanimate' this material with contemporary communities. The project has created two related data set: (i) the Women, Risk and AIDS Project data set 1988-90 (ii) the Reanimating Data set 2018-2021

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

The U.S. AI Training Dataset Market size was valued at USD 590.4 million in 2023 and is projected to reach USD 1880.70 million by 2032, exhibiting a CAGR of 18.0 % during the forecasts period. The U. S. AI training dataset market deals with the generation, selection, and organization of datasets used in training artificial intelligence. These datasets contain the requisite information that the machine learning algorithms need to infer and learn from. Conducts include the advancement and improvement of AI solutions in different fields of business like transport, medical analysis, computing language, and money related measurements. The applications include training the models for activities such as image classification, predictive modeling, and natural language interface. Other emerging trends are the change in direction of more and better-quality, various and annotated data for the improvement of model efficiency, synthetic data generation for data shortage, and data confidentiality and ethical issues in dataset management. Furthermore, due to arising technologies in artificial intelligence and machine learning, there is a noticeable development in building and using the datasets. Recent developments include: In February 2024, Google struck a deal worth USD 60 million per year with Reddit that will give the former real-time access to the latter’s data and use Google AI to enhance Reddit’s search capabilities. , In February 2024, Microsoft announced around USD 2.1 billion investment in Mistral AI to expedite the growth and deployment of large language models. The U.S. giant is expected to underpin Mistral AI with Azure AI supercomputing infrastructure to provide top-notch scale and performance for AI training and inference workloads. .

Success.ai’s Governmental and Congressional Data with Contact Data for Government Professionals Worldwide provides businesses, organizations, and institutions with verified contact information for key decision-makers in public sector roles. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles for government officials, administrators, policy advisors, and other influential leaders. Whether you’re targeting local municipalities, national agencies, or international government bodies, Success.ai delivers accurate, up-to-date data to help you engage effectively with public sector stakeholders.

Why Choose Success.ai’s Government Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles of government professionals worldwide.

3. AI-driven validation ensures 99% accuracy, giving you confidence in the reliability and precision of the data.

4. Global Reach Across Public Sectors

5. Includes profiles of elected officials, policy advisors, department heads, procurement managers, and regulatory authorities.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East, enabling true global engagement.

7. Continuously Updated Datasets

8. Real-time updates ensure your outreach remains timely, relevant, and aligned with current roles and responsibilities.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring ethical, lawful use of all contact data.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes government decision-makers and influential public sector leaders worldwide.
• 50M Work Emails: AI-validated for accuracy and easy communication.
• 30M Company (Agency/Institution) Profiles: Offers insights into various government departments, agencies, and organizations.
• 700M Global Professional Profiles: Enriched datasets to support a wide range of outreach and strategic initiatives.

Key Features of the Dataset:

1. Government Decision-Maker Profiles
2. Identify and connect with officials responsible for policy-making, budget approvals, program implementation, and public procurement.

3. Engage with professionals who influence legislation, infrastructure projects, and community development initiatives.

4. Advanced Filters for Precision Targeting

5. Filter by geographic jurisdiction, agency type, policy focus, job title, and more to reach the right government professionals.

6. Tailor your campaigns to align with specific public interests, regulatory frameworks, or service areas.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data, providing deeper insights that help you tailor your messaging and improve engagement success rates.

Strategic Use Cases:

1. Policy and Advocacy Outreach
2. Reach policymakers, advisors, and regulators to advocate for policy changes, present research findings, or share best practices.

3. Engage with officials who have the authority to influence regulations and legislative outcomes.

4. Procurement and Vendor Relations

5. Connect with procurement managers and government buyers seeking solutions, products, or services.

6. Present technology, infrastructure, or consulting offerings to decision-makers managing public tenders and supplier relationships.

7. Public-Private Partnerships

8. Identify and connect with key stakeholders involved in PPP initiatives, infrastructure projects, and long-term strategic collaborations.

9. Expand your network within government circles to foster joint ventures and co-development opportunities.

10. Market Research and Strategic Planning

11. Utilize government contact data for in-depth market research, stakeholder analysis, and feasibility assessments.

12. Gather insights from regulators, policy experts, and department heads to inform business strategies.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access premium-quality verified data at competitive prices, ensuring you achieve the best value for your outreach efforts.

3. Seamless Integration

4. Integrate verified government contact data into your CRM or marketing platforms via APIs or customizable downloads, streamlining your data management.

5. Data Accuracy with AI Validation

6. Count on 99% accuracy to inform your decision-making and improve the effectiveness of each interaction.

7. Customizable and Scalable Solutions

8. Tailor datasets to specific government tiers, agency types, or policy areas to meet unique organizational requirements.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance your existing records with verified government contact data, refining targeting and personalization efforts.

3. Lead Generation API

4. Automate lead generation, ensuring efficient scaling of your outreach and saving time a...

ISAR is the first global severe asthma registry; a joint initiative where national registries (both newly created and pre-existing) retain ownership of their own data but open their borders and share data with ISAR for ethically approved research purposes. The ISAR initiative is conducted by Optimum Patient Care Global Limited (OPC), with scientific oversight from the ISAR Core Steering Committee (ISC), academic support from the Respiratory Effectiveness Group (REG), ethical governance from the Anonymized Data Ethics & Protocol Committee (ADEPT), co-funding from OPC and AstraZeneca since May 2017. Prospective patient recruitment by 2022 is 13,150 patients with severe asthma.

The data collection included 87 qualitative interviews / focus groups (semi-structured and/or flexible / conversational / creative) with 143 people: 58 young people who engage in youth work, 59 youth workers and managers, and 26 policy makers, influencers and informants. The data focuses primarily on the value of youth work and its evaluation. The young people, youth workers and managers were from eight open youth work settings around England, selected to represent the diversity of open youth work (youth clubs in purpose built centres and shared spaces; detached / street based youth work; and open youth work with specific groups: trans young people, girls, and boys). The policy makers, influencers and informants were mostly from England. One was from Scotland and five from the USA; these perspectives were sought for comparative and international learning purposes. In addition, the researchers engaged in 73 sessions of participant observation. 63 of these were in the eight youth work settings mentioned above. The remaining ten were policy-related events. The fieldnotes and a small number of interview / focus group transcripts are not included in the shared dataset, either for ethical reasons (e.g. if it was not feasible to anonymise them or to redact sensitive data), or because participants opted out of data sharing.This research investigated the policy and practice of evaluation and accountability in youth work. It collaborated with young people, youth workers, managers, funders and policy makers/influencers, to understand the effects of impact measurement, and develop approaches to evaluation that are congruent with youth work practice. This three-year research project involved 143 participants in 87 qualitative interviews and focus groups, including flexible and creative approaches to interviewing (e.g. photovoice, peer interviewing, music elicitation). The researchers also engaged in extensive participant observation in eight open youth work settings around England (youth clubs, detached / street-based youth work, and youth work aimed at specific groups e.g. trans young people, girls, boys). The study aims to find out how the youth impact agenda is implemented in practice, and how impact processes are experienced and perceived by young people and youth workers. Interviews include the perspectives of policy makers and influencers in the UK and USA, to explore how and why 'youth impact' has become so important at this time. The study took a qualitative approach based on 87 interviews and focus groups with 143 young people, youth workers and policy influencers in England (16 of whom took part in two or more interviews or focus groups), alongside 73 sessions of participant observation. The research took place in four phases. Phase 1 involved interviews with 13 policy makers and influencers and participant observation in 10 policy-related events in 2018. Phase 2 took place in eight open youth work settings, purposively selected to encompass a diversity of youth work approaches, locations, and organisation types. This involved an average of four visits to each of eight youth work settings in the first half of 2019, and included participating in youth work sessions, debriefs and team meetings, alongside interviews with managers and administrators, and focus groups with youth workers and young people. This phase included 14 interviews and 14 focus groups with 87 participants. Phase three took place from December 2019 to October 2020, focusing in depth on two of the Phase 2 organisations. This enabled us to build a deeper contextualised understanding of evaluation and monitoring in these contrasting settings over time. Our longer engagement in these settings enabled greater trust, fluidity, collaboration and creativity. Data collection included a recorded tour of a youth club; sharing and discussion of photographs and songs; and a ‘paper chatterbox’ with questions selected by young people. This research phase was impacted by the Covid 19 pandemic and some participant observation, interviews and focus groups took place online. This phase included 28 interviews / focus groups with 40 participants. Phase 4 involved 15 online semi-structured interviews with ten policy makers and influencers from the England context, and five expert informants from the USA context.

Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics. (2023-07-06) Subject

The dataset contains around 1600 images depicting a particular interior style. The photos belong to one of eight classes: rustic, industrial, classic, vintage, modernist, art-deco, scandinavian, glamour.

The source of the dataset is Houzz.com. The images were downloaded from the website and grouped into folders.

You may use the dataset under the following terms:

Research and Development Purposes Only: Access to the dataset hosted on Zenodo is granted exclusively for research and development purposes. Users are required to clearly state their intention for using the dataset in this context.

Acknowledgment and Citation: Users must commit to providing proper acknowledgment and citation of the dataset in their research or development work. They should include the dataset's DOI and a reference to the original source in all publications, presentations, or reports derived from the dataset.

No Commercial Use: The dataset is not to be used for any commercial, for-profit, or financially exploitative purposes. Users must refrain from any activities that generate direct monetary gains from the dataset.

Ethical Use: Users are required to use the dataset in a manner consistent with ethical research practices. This includes respecting privacy, complying with relevant laws and regulations, and ensuring that the use of the data does not harm individuals, groups, or communities.

No Redistribution: Users are strictly prohibited from redistributing the dataset to third parties without prior written consent from the dataset owner. Any sharing of the dataset should be done solely for collaboration within the context of the research or development project.

Non-Discrimination: Access to the dataset should not be denied or granted based on factors such as race, ethnicity, gender, religion, nationality, or any other discriminatory criteria. All requests for access will be evaluated solely based on the justification provided by the user.

No Charge for Access: Users will not be charged any fees for accessing the data hosted on Zenodo. Access is provided free of charge, and users should not be required to make any payments to obtain or use the dataset.

Compliance with Zenodo's Terms of Use: Users are expected to comply with Zenodo's terms of use, including any additional terms or policies specific to the platform

Success.ai’s Education Marketing Data offers businesses and organizations direct access to verified contact details for educators, administrators, and marketing professionals in the education sector. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles, ensuring precise and meaningful connections with decision-makers at schools, universities, training centers, and educational service providers. By using continuously updated and AI-validated data, Success.ai empowers you to engage with the right contacts and drive targeted marketing campaigns, recruitment efforts, and partnership opportunities within the education landscape.

Why Choose Success.ai’s Education Marketing Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and social profiles of school administrators, university professors, department heads, and education marketers.
   • AI-driven validation ensures 99% accuracy, enabling confident outreach and reducing wasted efforts.

2. Global Reach Across Education Segments

   • Includes contacts from K-12 schools, higher education institutions, vocational training centers, e-learning platforms, and professional certification organizations.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a broad spectrum of educational institutions and markets.

3. Continuously Updated Datasets

   • Real-time updates guarantee that your contact data remains current, reflecting changes in roles, institutional structures, and academic priorities.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring your outreach is both ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes education sector leaders, influencers, and key decision-makers.
• 50M Work Emails: AI-validated for seamless communication and reduced bounce rates.
• 30M Company (Institution) Profiles: Gain insights into school types, program offerings, and organizational structures.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, competitive benchmarking, and strategic planning.

Key Features of the Dataset:

1. Education Decision-Maker Profiles

   • Identify and connect with principals, superintendents, deans, admissions directors, marketing managers, and department heads shaping curriculum, enrollment, and academic initiatives.

2. Advanced Filters for Precision Targeting

   • Filter by institution type, geographic region, academic level, specialty programs, or job function to refine your outreach and campaigns.
   • Tailor messaging to align with unique educational needs, cultural contexts, and policy frameworks.

3. AI-Driven Enrichment

   • Profiles are enriched with actionable data, offering insights into institutional priorities, enrollment trends, and academic focal points, enabling more personalized and effective engagement.

Strategic Use Cases:

1. Marketing and Enrollment Campaigns

   • Target admissions and marketing professionals at universities, colleges, and language schools to promote your educational products, tutoring services, or learning management systems.
   • Craft campaigns that resonate with educators’ challenges, such as student retention, curriculum innovation, or digital learning adoption.

2. EdTech and Resource Partnerships

   • Connect with decision-makers evaluating new technologies, software platforms, and resource providers to enhance teaching and learning experiences.
   • Position your EdTech solutions to solve institutional pain points like remote learning effectiveness or data-driven student success strategies.

3. Academic Collaboration and Research

   • Identify contacts in academic research, curriculum development, or accreditation bodies to foster partnerships, co-develop programs, or share research findings.
   • Engage with administrators overseeing funding, grants, and educational policy to influence institutional decision-making.

4. Recruitment and Talent Acquisition

   • Find HR professionals and department heads seeking qualified instructors, administrative staff, or specialized educators.
   • Offer recruitment and professional development services to institutions aiming to attract top-tier academic talent.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring cost-effective growth and strategic advantage in education-focused outreach.

2. Seamless Integration

   • Integrate verified contact data into your CRM or marketing automation tools using APIs or downloadable formats for efficient data management.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to inform decisions, refine targeting, and enhance campai...

The Everyday Childhoods collection is a qualitative longitudinal dataset that was collected by researchers from the Universities of Sussex and Brighton and the Open University during 2013-15. The initial project, called ‘Face 2 Face: Tracing the real and the mediated in children’s cultural worlds’ (F2F) was funded by an NCRM Methodology Innovation award. The primary aim of the project was to explore how technologies documented and mediated the everyday in children's daily lives. The F2F project generated the majority of the data contained in this collection and the dataset comprises data from two research panels: firstly, a younger panel (the 'extensive' panel) of children aged 7-8 years (n=6) who had previously been involved with their families in an ESRC funded study of new motherhood ('The Making of Modern Motherhoods: Memories, Representations, Practices'). Their geographical location ranged across the South, South East and South West of England. Secondly, an older panel (the 'intensive' panel) of children aged 10-15 years (n=7) were recruited for the first time in this study. Their geographical location was focused in the South East of England. This latter sample were recruited to illustrate a diversity of youth experiences and identities, including along intersectional lines of ethnicity, religion, dis/ability, urban/rural locality, and economic background. Over the course of 12 months, both groups of children took part in a series of regular research activities aimed at capturing their everyday lives.

Face 2 Face: Tracing the Real and the Mediated in Children’s Cultural Worlds (2013-14) was a 12-month methodological innovation project funded by the ESRC’s National Centre for Research Methods. The study documented thirteen children and young people’s everyday lives over a 12-month period, focusing on how new media technologies were infused in their everyday lives and relationships. The research team worked with two panels: a group of 8 year olds who were part of an established longitudinal study of new motherhood and had been followed since before birth, and a newly established panel of 11-15 year olds. Using a combination of biographical, ethnographic and digital/material methods, the research team worked closely with participants and their families to document their everyday lives. One of the key outputs of the research was a set of public multimedia documents that experimented with using data from the study to depict the children's lives. These multi-modal documents used the digital sound recordings, photographs, ethnographic descriptions and other data captured during the different phases of the study. Ethical practice around the documentation and curation of data about children's lives was a key strand of the study, and were further developed through an AHRC funded follow-up study: 'Curating Childhoods'. This study enabled the to follow up questions about the use of research data in digital age into the domains of archiving and data sharing. As part of this follow up project, participants from the Face 2 Face study were invited to take part in a one-day workshop at the Mass Observation Archive to discuss the future archiving and re-use of their data.

This is the metadata for a clinical dataset entitled the The ARTERIAL US Study (A pReTERm Infants’ cArdiovascular deveLopment: An Ultrasound Study). We collected cardiovascular ultrasound data on the geometry, heart size, blood vessel diameters) and function (Doppler flow waveforms) of term and preterm hearts and vasculature. Study design: The ARTERIAL US Study is a single-centre prospective observational cohort study. Study synopsis Participants: 1. Term group: babies born at or after 37+0 weeks gestation 2. Late preterm group: babies born at or after 34+0 and before 37+0 weeks gestation Primary Outcome(s): Haemodynamic status as computed by the computational model of the neonatal cardiovascular system Sample Size: 15 term and 10 late preterm Study Setting: Auckland City Hospital, Te Toka Tumai Auckland (formerly Auckland District Health Board) Eligibility criteria Inclusion criteria: Born at or after at or after 37+0 weeks gestation (term group) or born at or after 34+0 and before 37+0 weeks gestation (late preterm group), Parental consent Exclusion criteria: Known medical conditions or cardiovascular abnormalities. Data collection Methods: Babies will have an ultrasound examination within 48 hours of birth and again three to six weeks later weeks later (i.e., at term equivalent postmenstrual age). Data collection included clinical data collection (data from the medical records about the following clinical factors: antenatal admission to hospital, gestational diabetes mellitus, antenatal infection, placental:fetal weight ratio, exposure to antenatal corticosteroids and magnesium sulphate, risk factors and primary reason for preterm birth (including pre-eclampsia, chorioamnionitis and fetal growth restriction), age at scan, sex, gestational age at birth, birth weight and length, head circumference at birth, APGARs, delayed cord clamping, postnatal steroid administration), anthropometric measurements and ultrasound measurements. Data availability Data and associated documentation from participants who have consented to future re-use of their data are available to other users under the data sharing arrangements provided by the University of Auckland’s Human Health Research Services (HHRS) platform (https://research-hub.auckland.ac.nz/subhub/human-health-research-services-platform). The data dictionary and metadata are published on the here. Researchers are able to use this information and the provided contact address (dataservices@auckland.ac.nz) to request a de-identified dataset through the HHRS Data Access Committee. Data will be shared with researchers who provide a methodologically sound proposal and have appropriate ethical approval, where necessary, to achieve the research aims in the approved proposal. Data requestors are required to sign a Data Access Agreement that includes a commitment to using the data only for the specified proposal, not to attempt to identify any individual participant, a commitment to secure storage and use of the data, and to destroy or return the data after completion of the project. The HHRS platform reserves the right to charge a fee to cover the costs of making data available, if needed, for data requests that require additional work to prepare.

Context

There's a story behind every dataset and here's your opportunity to share yours.

As the spread of the novel covid-19 continues to run into countries it is important for us to keep records of every Information on it. Therefore, this dataset is built basically to cover the update from Africa.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. It contains Information on the dates the cases were recorded across Africa. Detailing the death, confirmed and recovery cases in each country.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Ethical AI Club John Hopkins University Runmila Institute WHO CDC Ghana Health Service

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered? We should be able to see contributors answering questions about how Africa should prepare and put in the right measures to contain the spread. A better understanding from the Data scientists.

This dataset is generated from 20 households in the south east of England, as part of a trial that used digital sensors for observational purposes in social research. While sensor-generated data is omitted from this dataset, the trial produced interviews, questionnaires, time-use diaries and ethnographic notes covering various aspects of the trial, including household configurations and practices, study participation (intrusion, burden, meaningfulness) and records of living with sensors.

What actually happens within households? We know that men are increasingly sharing in domestic duties and parenting; but does this mean that these activities are being done with their partners or are they taking turns? Do families eat together and talk to each other, or do they have separate meals in different rooms while talking on social media to their friends? It is hard to observe households, and research on these issues is done through self-reporting, with people answering questions and filling in diaries, or with highly invasive methods such as video recording. There is another way.

Digital devices are becoming more sophisticated. A modern mobile phone can measure position and movement, as well as what the phone is being used for. Many people wear sensors for heart rate, sleeping patterns, and physical activity. And fixed sensors in houses can be simply plugged in to measure sound and energy use. Using such sensors effectively would reduce the need for questionnaires and interviews, reducing the amount of work for respondents and providing potentially more accurate reporting.

However, there are technical problems to be solved. What can be measured by these devices? How can the data be converted into meaningful descriptions of activities? How reliable are these descriptions? There are also ethical concerns. How can the datasets be securely stored and for how long? How does consent work if people forget the devices are there? When should consent be obtained from people who are monitored but not intentionally included in the research, such as visitors?

This project will examine these technical and ethical issues. We will develop guidelines for social researchers who want to use digital sensing devices in their research. These will be based on expert advice and discussion with members of the general public, as well as the experience of household members and researchers in a trial study. The data collected in the trial study will be used to compare, contrast and integrate the use of sensor devices with existing research methods. The trial data and comparison of methods will be the foundation to develop analysis tools that help researchers to interpret and understand the rich data that can be collected with these methods, to answer questions about what happens within households.

Subjective data models dataset

This dataset is comprised of data collected from study participants, for a study into how people working with biological data perceive data, and whether or not this perception of data aligns with a person's experiential and educational background. We call the concept of what data looks like to an individual a "subjective data model".

Todo: link paper/preprint once published.

Computational python analysis code: https://doi.org/10.5281/zenodo.7022789 and https://github.com/yochannah/subjective-data-models-analysis

Files

Transcripts of the recorded sessions are attached and have been verified by a second researcher. These files are all in plain text .txt format. Note that participant 3 did not agree to sharing the transcript of their interview. Interview paper files This folder has digital and photographed versions of the files shown to the participants for the file mapping task. Note that the original files are from the NCBI and from FlyBase. Videos and stills from the recordings have been deleted in line with the Data Management Plan and Ethical Review. anonymous_participant_list.csv shows which files have transcripts associated (not all participants agreed to share transcripts), what the order of Tasks A and B were, the date of interview, and what entities participants added to the set provided (if any). See the paper methods for more info about why entities were added to the set. cards.txt is a full list of the cards presented in the tasks. background survey and background manual annotations are the select survey data about participant background and manual additions to this where necessary, e.g. to interpret free text. codes.csv shows the qualitative codes used within the transcripts. entry_point.csv is a record of participants' identified entry points into the data. file_mapping_responses shows a record of responses to the file mapping task.