Individuals have the right to access their personal data held by private companies. This operation can be started by different types of data subjects. A 2020 poll conducted among UK managers showed that ** percent of the requests came from employees or ex-employees. Another ** percent of Data Subject Access Requests (DSAR) were submitted by customers.

Information: Subject Series - Misc Subjects: Receipts by type of Dissemination Media for the U.S.: 2012.

Vehicle Data Subject Access Requests Market Outlook

According to our latest research, the Global Vehicle Data Subject Access Requests market size was valued at $1.25 billion in 2024 and is projected to reach $5.89 billion by 2033, expanding at a robust CAGR of 18.7% during the forecast period of 2025–2033. The primary factor fueling this remarkable growth is the increasing emphasis on data privacy and regulatory compliance in the automotive sector, particularly as vehicles become more connected and generate vast amounts of personal and operational data. The proliferation of connected vehicles and the implementation of stringent data protection laws such as GDPR and CCPA are compelling automotive stakeholders to adopt advanced solutions for managing and responding to data subject access requests (DSARs), ensuring transparency and user rights in vehicle data handling.

Regional Outlook

North America currently commands the largest share of the global Vehicle Data Subject Access Requests market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature automotive industry, widespread deployment of connected vehicles, and proactive regulatory frameworks surrounding data privacy. The United States, in particular, has witnessed a surge in DSAR-related services following the enactment of the California Consumer Privacy Act (CCPA) and similar state-level regulations, compelling OEMs, fleet operators, and insurers to invest in robust data management and reporting systems. Furthermore, the presence of leading technology providers and a high rate of cloud adoption have accelerated the integration of advanced DSAR solutions across automotive enterprises, reinforcing North America’s leadership in this space.

The Asia Pacific region is poised to be the fastest-growing market for Vehicle Data Subject Access Requests, projected to register a staggering CAGR of 22.4% between 2025 and 2033. This accelerated growth is driven by rapid digital transformation in the automotive sector, burgeoning vehicle sales, and increasing awareness of data privacy rights among consumers. Countries like China, Japan, and South Korea are investing heavily in connected vehicle infrastructure and smart mobility solutions, which in turn necessitate robust data governance and compliance mechanisms. Additionally, government initiatives to harmonize data protection standards and the rising adoption of electric and autonomous vehicles are further propelling the demand for DSAR solutions across the region.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Vehicle Data Subject Access Requests solutions, albeit at a slower pace due to infrastructural and regulatory challenges. While these regions represent a smaller share of the global market—collectively accounting for less than 15% in 2024—their potential for future adoption is significant, especially as local governments begin to implement data privacy laws and automotive digitalization initiatives. However, limited awareness, fragmented data ecosystems, and a lack of standardized compliance frameworks currently pose hurdles to widespread DSAR adoption. Nevertheless, as international automotive brands expand their footprint and regulatory harmonization improves, these markets are expected to contribute meaningfully to global growth over the next decade.

Report Scope

Transformed E-commerce Product Data: Ready for Analysis!

Sharpen your data analysis skills with this cleaned and enhanced e-commerce product dataset. This dataset was originally spread across 20+ CSV files with over 80,000 products, this dataset now offers a streamlined single CSV for you to focus on analysis.

What's Inside:

Clean and Consistent Data: Missing values, formatting inconsistencies, and errors have been addressed, ensuring a foundation for reliable analysis.

Organized Product Information: Explore a variety of product categories, including:

• Apparel & Accessories
• Electronics
• Home & Kitchen
• Men's Clothing
• Women's Clothing
• Swimsuits and Sleepwear
• And more!

Detailed Product Records:

• Product Title
• Category
• Price
• Discount Information
• Product subcategory
• Product rank
• Quantity sold
• Color count
• Image source URL

Dive right into creating new features from existing data, such as:

• Extracting keywords from titles and descriptions
• Deriving price categories
• Calculating average discounts

Accommodation and Food Services: Subject Series - Misc Subjects: Primary Type of Food Service for the U.S. and States: 2012.

To inform efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by National Institutes of Health (NIH)-funded researchers. Of particular interest is characterizing those datasets that are not deposited in a known data repository or registry, e.g., those for which a related journal article does not indicate that underlying data have been deposited in a known repository. Such “invisible” datasets comprise the “long tail” of biomedical data and pose significant practical challenges to ongoing efforts to improve discoverability of and access to biomedical research data. This study identified datasets used to support the NIH-funded research reported in articles published in 2011 and cited in PubMed® and deposited in PubMed Central® (PMC). After searching for all articles that acknowledged NIH support, we first identified articles that contained explicit mention of datasets being deposited in recognized repositories. Thirty members of the NIH staff then analyzed a random sample of the remaining articles to estimate how many and what types of datasets were used per article. Two reviewers independently examined each paper. Each dataset is titled Bigdata_randomsample_xxxx_xx. The xxxx refers to the set of articles the annotator looked at, while the xxidentifies the annotator that did the analysis. Within each dataset, the author has listed the number of datasets they identified within the articles that they looked at. For every dataset that was found, the annotators were asked to insert a new row into the spreadsheet, and then describe the dataset they found (e.g., type of data, subject of study, etc.). Each row in the spreadsheet was always prepended by the PubMed Identifier (PMID) where the dataset was found. Finally, the files 2013-08-07_Bigdatastudy_dataanalysis, Dataanalysis_ack_si_datasets, and Datasets additional random sample mention vs deposit 20150313 refer to the analysis that was performed based on each annotator's analysis of the publications they were assigned, and the data deposits identified from the analysis.

Categories for Subject Matter - ARCHIVED

For the new LA City Events dataset (refreshed daily), see https://data.lacity.org/A-Prosperous-City/LA-City-Events/rx9t-fp7k

National level pupil entries and achievements across subjects by establishment type for pupils at the end of KS4 in 2024/25.

Dataset

This dataset was created by hamza737

Released under CC0: Public Domain

Contents

Content

This dataset contains a cleaned version of this dataset https://www.kaggle.com/rikdifos/credit-card-approval-prediction on credit cards.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Murlo" data publication.

Finance and Insurance: Subject Series - Misc Subjects: Type of Loan Services Income for the U.S.: 2012.

A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.

32 million - Science Subjects Questions Text Parsing And Processing Data, including mathematics, physics, chemistry and biology in primary, middle and high school and university. Each questions contain title, answer, parse, type, subject, grade. The dataset can be used for large model subject knowledge enhancement tasks.

Content

Science subjects questions text;

Data Size

About 32 million;

Data Fields

Contains title, answer, parse, subject, grade, question type;

Subject categories

Primary school, middle school, high school and university science subjects;

Format

Jsonl;

Language

Chinese;

Data processing

Subject, questions, parse and answers were analyzed, formula conversion and table format conversion were done, and content was also cleaned）

The source of the data in this table are provinces and CBS offers them as a service as open data.

Statistics Netherlands (CBS) receives data from provinces in the context of the Third Party Information (Iv3) reports. The data in the table have not been edited by Statistics Netherlands. This type of data is also referred to as 'unprocessed data'. CBS bears no responsibility for the quality of the data. The data in Statistics Netherlands' own publications do not have to be traced back one-on-one to the data in this table.

The table contains raw Iv3 data from all reporting types of one reporting year. The types of reports are the budget, the four quarters and the annual accounts. If a province has not provided Iv3 data for a report type, then this province is included in the table, but each cell has the value '.' (missing).

The codes used in the table for the categories on the one hand and the task fields and balance sheet items on the other hand, as well as their meaning, are derived from the 'Decree on the budget and accountability of provinces and municipalities' (BBV) of the Ministry of the Interior and Kingdom Relations. The BBV contains, among other things, the regulations for the deliveries of Iv3 data to CBS.

For each type of report, all reports received so far are published at the same time at two points in time. The reason for placing the data a second time is that CBS gives the provinces the opportunity to provide an improved Iv3 dataset. The data that is placed the first time has the value '1st placement' in the topic 'Place'. The data that is placed the second time has the value '2nd placement'.

Data available from: 2025.

Status of figures The figures in this table are final upon publication (i.e., subject to exceptions, once published data are no longer updated).

Changes as of 2 December 2024: None, this is a new table. Figures for the first allocation of the 2025 budget are included.

When will there be new figures? The time of publication of new figures for a type of report depends on the deadline for submission to Statistics Netherlands that applies to the type of report in question. For budgets for year j, the deadline for submission is 14 November in the year preceding the budget year (j-1). For quarterly data for the first, second and third quarters of year j, this is one month after the end of the quarter. For submission of the fourth quarter of year j, a deadline of 14 February in the year following the reporting year (j+1) applies. Finally, for the annual accounts for year j, this date is 14 July in the year following the reporting year (j+1). All reports received for a report type are published at the same time. This publication happens twice. The first time is 10 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than 5 days after the deadline for submission. The second time is 70 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than two months after the deadline for submission. The distinction between the first and the second placement can be seen in the subject.

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.

statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

    Fieldname
    Description




    abstract_data
    A brief synopsis or abstract about the learning resource


    abstract_format
    Declaration for how the abstract description will be represented.


    access_conditions
    Conditions upon which the resource can be accessed beyond cost, e.g., login required.


    access_cost
    Yes or No choice stating whether othere is a fee for access to or use of the resource.


    accessibililty_features_name
    Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.


    accessibililty_summary
    A human-readable summary of specific accessibility features or deficiencies.


    author_names
    List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system


    author_org
    - name
    - name_identifier
    - name_identifier_type



    - Name of organization authoring the learning resource.
    - The unique identifier for the organization authoring the resource.
    - The identifier scheme associated with the unique identifier for the organization authoring the resource.

authors - givenName - familyName - name_identifier - name_identifier_type

    - Given or first name of person(s) authoring the resource.
    - Last or family name of person(s) authoring the resource.
    - The unique identifier for the person(s) authoring the resource.
    - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.



    citation
    Preferred Form of Citation.


    completion_time
    Intended Time to Complete

contact - name - org - email

    - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - (excluded) Contact email address.



    contributor_orgs
    - name
    - name_identifier
    - name_identifier_type
    - type
    - Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.
    - The unique identifier for the organization contributing to the resource.
    - The identifier scheme associated with the unique identifier for the organization contributing to the resource.
    - Type of contribution to the resource made by an organization.


    contributors
    - familyName
    - givenName
    - name_identifier
    - name_identifier_type

• Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.

contributors.type

Type of contribution to the resource made by a person.

    created
    The date on which the metadata record was first saved as part of the input workflow.


    creator
    The name of the person creating the MD record for a resource.


    credential_status
    Declaration of whether a credential is offered for comopletion of the resource.

ed_frameworks - name - description - nodes.name

    - The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.
    - A description of one or more subcategories of an educational framework to which a resource is associated.
    - The name of a subcategory of an educational framework to which a resource is associated.


    expertise_level
    The skill level targeted for the topic being taught.


    id
    Unique identifier for the MD record generated by the system in UUID format.


    keywords
    Important phrases or words used to describe the resource.


    language_primary
    Original language in which the learning resource being described is published or made available.


    languages_secondary
    Additional languages in which the resource is tranlated or made available, if any.


    license
    A license for use of that applies to the resource, typically indicated by URL.


    locator_data
    The identifier for the learning resource used as part of a citation, if available.


    locator_type
    Designation of citation locatorr type, e.g., DOI, ARK, Handle.


    lr_outcomes
    Descriptions of what knowledge, skills or abilities students should learn from the resource.


    lr_type
    A characteristic that describes the predominant type or kind of learning resource.


    media_type
    Media type of resource.


    modification_date
    System generated date and time when MD record is modified.


    notes
    MD Record Input Notes


    pub_status
    Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.


    published
    Date of first broadcast / publication.


    publisher
    The organization credited with publishing or broadcasting the resource.


    purpose
    The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.


    rating
    The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.


    ratings
    Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.


    resource_modification_date
    Date in which the resource has last been modified from the original published or broadcast version.


    status
    System generated publication status of the resource w/in the registry as a yes for published or no for not published.


    subject
    Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.


    submitter_email
    (excluded) Email address of person who submitted the resource.


    submitter_name
    Submission Contact Person


    target_audience
    Audience(s) for which the resource is intended.


    title
    The name of the resource.


    url
    URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.


    usage_info
    Descriptive information about using the resource, not addressed by the License information field.


    version
    The specific version of the resource, if declared.

Decoding functional category of source code from the brain (fMRI on Java program comprehension)

Original paper

TODO: write arXiv(?) URL of the paper here.

Overview

In this study, fMRI data was recorded while subjects were categorizing source code snippets into one of four functional categories. The experiment consisted of six separate runs (36 trials plus one dummy trial for each run) and a total of 72 Java code snippets were each presented three times. In each trial, a Java code snippet was displayed for ten seconds after a fixation-cross presentation for two seconds. Then, subjects classified the given code snippet into one of four category classes within four seconds by pressing a button. We recruited top- and middle-rated programmers as well as novice controls to cover a wide range of programming expertise using programmers' rate in the competitive programming contest 'AtCoder'. fMRI data in each subject were used to train and evaluate models (decoders) to predict functional category or subcategory of seen Java code snippets. Searchlight-based decoding accuracies were assessed to identify the brain regions that contribute expert programmers' outstanding performances on program comprehension.

Source code for preprocessing and analyses is available at GitHub (TODO: write repository URL here).

Dataset

MRI files

This dataset contains fMRI data from twenty-nine subjects ('sub-01', 'sub-02', ..., 'sub-29'). Each subject data contains anatomical and functional MRI data. Functional scans were collected over six scanning runs.

The functional EPI scans covered the entire brain (TR, 2000 ms; TE, 30 ms; flip angle, 75°; voxel size, 2 × 2 × 2.01 mm; FOV, 192 × 192 mm; slice gap, 0 mm). The dataset also includes a T1-weighted anatomical reference image for each subject (TR, 2530 ms; TE, 3.26 ms; flip angle, 9°; voxel size, 1.0 × 1.0 × 1.0 mm; FOV, 256 × 256 mm). The T1-weighted images were scanned only once for each subject. The T1-weighted images were defaced using mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface). All DICOM files are converted to Nifti by dcm2niix (version v1.0.20190902).

Note: We used MRI data from thirty subjects in the original paper. Twenty-nine subjects approved to open their MRI data to the public but one subject declined. Thus, we published the MRI data only from subjects who approved to make it open.

Subject information

The subject information file ('participants.tsv') denote the background information of each subject (age, sex, handedness, etc.). You can find what each column of the subject information files represents in './participants.json'.

Task event files

Task event files (‘sub-*_func_task-ProgramCategorization_run-*_events.tsv’) denote recorded event (stimuli code, subject responses, etc.) during fMRI runs. You can find what each column of the task event files represents in './task-ProgramCategorization_events.json'.

Java code snippets as the experimental stimuli

Java code snippets used in the study were stored in the stimuli directory ('./stimuli'). They were collected from an open codeset provided by AIZU ONLINE JUDGE (http://judge.u-aizu.ac.jp/onlinejudge/) and preprocessed by the authors to normalize indentation styles and names of user-defined functions. The 'stim_file' column in the task event files indicate one of the Java code snippets in the stimuli directory to specify which code snippet was used in each trial of the experiment.

Contact

• Email: takatomi-k@is.naist.jp

Global Data Center Networking Market size valued at US$ 23.03 Billion in 2023, set to reach US$ 44.09 Billion by 2032 at a CAGR of about 7.48% from 2024 to 2032

Environmental Pollution Incident data filtered for Categories 1 and 2. Details of environmental incidents within the remit of the Environment Agency are held on the National Incident Recording System (NIRS2). This dataset only includes substantiated completed and closed Environment Management incidents (predominantly pollution), where the environment impact level is either category 1 (major) or category 2 (significant) to at least 1 media (i.e. water, land or air). It is updated quarterly and provides a snapshot of data held in NIRS2. There is an inherent lag time in investigating and recording the necessary incident details to complete a record and recent incidents may not appear. The data may also be subject to change due to final QA and as further information becomes available. INFORMATION WARNING: Where these data indicate an incident occurred on a particular site or property no inference should be drawn that the site or property owner necessarily was responsible. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.