Help us provide the most useful data by completing our ODP User Feedback Survey for Child and Adult Care Food Program (CACFP) Data

About the Dataset
This data set contains claims information for meal reimbursement for sites participating in CACFP as child centers for the program year 2024-2025. This includes Child Care Centers, At-Risk centers, Head Start sites, Outside School Hours sites, and Emergency Shelters . The CACFP program year begins October 1 and ends September 30.

This dataset only includes claims submitted by CACFP sites operating as child centers.Sites can participate in multiple CACFP sub-programs. Each record (row) represents monthly meals data for a single site and for a single CACFP center sub-program.

To filter data for a specific CACFP center Program, select "View Data" to open the Exploration Canvas filter tools. Select the program(s) of interest from the Program field. A filtering tutorial can be found HERE

For meals data on CACFP participants operating as Day Care Homes, Adult Day Care Centers, or child care centers for previous program years, please refer to the corresponding “Child and Adult Care Food Programs (CACFP) – Meal Reimbursement” dataset for that sub-program available on the State of Texas Open Data Portal.

An overview of all CACFP data available on the Texas Open Data Portal can be found at our TDA Data Overview - Child and Adult Care Food Programs page.

An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page.

More information about accessing and working with TDA data on the Texas Open Data Portal can be found on the SquareMeals.org website on the TDA Food and Nutrition Open Data page.

About Dataset Updates
TDA aims to post new program year data by December 15 of the active program year. Participants have 60 days to file monthly reimbursement claims. Dataset updates will occur daily until 90 days after the close of the program year. After 90 days from the close of the program year, the dataset will be updated at six months and one year from the close of the program year before becoming archived. Archived datasets will remain published but will not be updated. Any data posted during the active program year is subject to change.

About the Agency
The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs, the Child and Adult Care Food Programs (CACFP), and the summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles.

For more information on these programs, please visit our website.
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Maryland's Open Data Program provides transparency so that businesses, residents, visitors, and civil servants can use state data to aid in decision making. The transparency of data helps improve accountability, as data owners share the responsibility for ensuring that data is accurate, up-to-date and available to the public. Each January, the Council on Open Data makes it's Annual Open Data Report available to the public. Please explore the annual reports below to see how Maryland continues to support Open Data.

Overview of school nutrition program data available on the Texas Open Data Portal from Texas Department of Agriculture

Instrument Incubator Program

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data.

Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set.

Exceptions to this are:

• Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence.
• Data from Gateway to Research is made available under an Open Government Licence (Version 3.0).

Contents

• Survey data & analysis: esrc_data-survey-analysis-data.zip
• Other data: esrc_data-other-data.zip
• Transcripts: esrc_data-transcripts.zip
• Data Management Plan: esrc_data-dmp.zip

Survey data & analysis

The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data.

The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled.

The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly.

A pdf copy of the survey questions is available on GitHub.

The survey data has been decoupled into:

• survey-results-key.csv - maps a question number and the responses to the actual question values.
• q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16).
• q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23).
• q17-institutions.csv - the institution/location of the respondent (Q17).
• q18-funding.csv - funding sources within the last 5 years (Q18).

Please note the code that has been used to do the analysis will not run with the decoupled survey data.

Other data files included

• CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered.
• DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021.
• projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence.
• locations.csv - latitude and longitude for the institutions in the cleaned locations.
• subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot.
• topics.csv - topic classification for the ESRC projects for the 24th February data snapshot.

Interview transcripts

The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts:

• 1269794877.md
• 1578450175.md
• 1792505583.md
• 2964377624.md
• 3270614512.md
• 40983347262.md
• 4288358080.md
• 4561769548.md
• 4938919540.md
• 5037840428.md
• 5766299900.md
• 5996360861.md
• 6422621713.md
• 6776362537.md
• 7183719943.md
• 7227322280.md
• 7336263536.md
• 75909371872.md
• 7869268779.md
• 8031500357.md
• 9253010492.md

Data Management Plan

The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

HHS responsibly shares “open by default” data with the public to democratize access to information, demystify the Department, and increase transparency through data sharing. HHS Open Data is non-sensitive data, meaning thousands of health and human services datasets are publicly available to fuel new business models, enable emerging technologies like AI, accelerate scientific discoveries, and inspire American innovation. This top-1000 HHS Open Data websites and resources page, dynamically generated from the Digital Analytics Program (DAP) provided by the U.S. General Services Administration (GSA), is driven by near-real-time user demand. GSA’s DAP helps federal agencies and the public see how visitors find, access, and use government websites, data, and services online. The below list filters DAP for only resources from HHS and includes all HHS Divisions. You may filter by individual HHS Divisions and columns.

This dataset lists out all software in use by NASA

This repository is currently being built. It contains data from the Software Factory (SF), a service unit at a large southwestern university providing software engineering services for funded research projects and university technology spinouts. SF was led by a professional software engineer, who managed student programmers using industry-standard engineering processes and organized in formal project-based teams. It operated for 144 weeks from late 2002 to early 2005, and had 79 participants, including the manager, employees, clients and researchers. Over this time, SF worked on 31 separate projects, developing applications for the social sciences, natural sciences, and education, and for internal use (such as an activity reporting system). SF was also a social science research project intended to collect data on perceived and observable communication networks in a naturalistic organizational setting over an extended period. SF data was used in a project funded by the National Science Foundation’s Science of Organizations program, and is being deposited here per NSF requirements.

Unlock the full potential of BBC broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

Dataset Features

Transcripts: Access detailed broadcast transcripts, including headlines, content, author details, and publication dates. Perfect for analyzing media framing, topic frequency, and news narratives across various programs. Program Schedules: Explore program schedules with accurate timing, show names, and related metadata to track news coverage patterns and identify trends. Topics and Keywords: Analyze categorized topics and keywords to understand content diversity, editorial focus, and recurring themes in news broadcasts. Multimedia Content: Gain access to videos, images, and related articles linked to each broadcast for a holistic understanding of the news presentation. Metadata: Includes critical data points like publication dates, last updates, content URLs, and unique IDs for easier referencing and cross-analysis.

Customizable Subsets for Specific Needs Our CNN dataset is fully customizable to match your research or analytical goals. Focus on transcripts for in-depth media framing analysis, extract multimedia for content visualization studies, or dive into program schedules for broadcast trend analysis. Tailor the dataset to ensure it aligns with your objectives for maximum efficiency and relevance.

Popular Use Cases

Media Analysis: Evaluate news framing, content diversity, and topic coverage to assess editorial direction and media focus. Transparency Studies: Analyze journalistic standards, corrections, and retractions to assess media integrity and accountability. Audience Engagement: Identify recurring topics and trends in news content to understand audience preferences and behavior. Market Analysis: Track media coverage of key industries, companies, and topics to analyze public sentiment and industry relevance. Journalistic Integrity: Use transcripts and metadata to evaluate adherence to reporting practices, fairness, and transparency in news coverage. Research and Scholarly Studies: Leverage transcripts and multimedia to support academic studies in journalism, media criticism, and political discourse analysis.

Whether you are evaluating transparency, conducting media criticism, or tracking broadcast trends, our BBC dataset provides you with the tools and insights needed for in-depth research and strategic analysis. Customize your access to focus on the most relevant data points for your unique needs.

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

This dataset contains FEMA applicant-level data for the Individuals and Households Program (IHP). All PII information has been removed. The location is represented by county, city, and zip code. This dataset contains Individual Assistance (IA) applications from DR1439 (declared in 2002) to those declared over 30 days ago. The full data set is refreshed on an annual basis and refreshed weekly to update disasters declared in the last 18 months. This dataset includes all major disasters and includes only valid registrants (applied in a declared county, within the registration period, having damage due to the incident and damage within the incident period). Information about individual data elements and descriptions are listed in the metadata information within the dataset.rnValid registrants may be eligible for IA assistance, which is intended to meet basic needs and supplement disaster recovery efforts. IA assistance is not intended to return disaster-damaged property to its pre-disaster condition. Disaster damage to secondary or vacation homes does not qualify for IHP assistance.rnData comes from FEMA's National Emergency Management Information System (NEMIS) with raw, unedited, self-reported content and subject to a small percentage of human error.rnAny financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting. rnCitation: The Agency’s preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnDue to the size of this file, tools other than a spreadsheet may be required to analyze, visualize, and manipulate the data. MS Excel will not be able to process files this large without data loss. It is recommended that a database (e.g., MS Access, MySQL, PostgreSQL, etc.) be used to store and manipulate data. Other programming tools such as R, Apache Spark, and Python can also be used to analyze and visualize data. Further, basic Linux/Unix tools can be used to manipulate, search, and modify large files.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.rnThis dataset is scheduled to be superceded by Valid Registrations Version 2 by early CY 2024.

Each year, the Department of Housing Preservation and Development (HPD) designates buildings for participation in the Underlying Conditions Program (Local Law 6 of 2013). These buildings are selected based on the number and severity of class "B" and "C" violations related to the presence of mold or water leaks issued by HPD within the past year and an inspection is attempted to verify conditions before the order is issued.

The home of Medicaid and CHIP open data provided by the Federal Government. Conduct research and design data visualizations using open data from Medicaid and the Children's' Health Insurance Program (CHIP) Data available on the following categories: • Drug Pricing and Payment • Enrollment • Quality • Eligibility • State Drug Utilization • Uncategorized

The City of Detroit Open Data Portal allows access to datasets, maps, charts, and files and documents on City of Detroit government operations and programs, public safety (Detroit Police Department, Detroit Fire Department, and Detroit Department of Homeland Security), property and parcels, schools and educational institutions, transportation, public health, and permits.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

Market research dataset covering growth of the global open-source software market, including benefits, adoption, and enterprise usage in 2025.

Overview

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 12 regions worldwide, with a particular focus on developing countries. About 4,500 paired optical and SAR images containing over 350,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.

IEEE GRSS Data Fusion Contest 2025

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.

Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images.

Please download dfc25_track2_test.zip and unzip it. It contains test images for the final test phase.

Benchmark code related to the DFC 2025 can be found at this Github repo.

The official leaderboard is located on the Codalab-DFC2025-Track II page.

Paper & Reference

Details of BRIGHT can be refer to our paper.

If BRIGHT is useful to research, please kindly consider cite our paper

@article{chen2025bright,
   title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response}, 
   author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
   journal={arXiv preprint arXiv:2501.06019},
   year={2025},
   url={https://arxiv.org/abs/2501.06019}, 
}

License

Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.

With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license.

The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.

The Changing Arctic Ocean (CAO) oceanographic dataset comprises data collected in the Arctic Ocean, including the Barents Sea and Fram Strait, as part of the Changing Arctic Ocean programme. The data were collected over multiple research cruises starting in June 2017. The majority of these cruises were conducted during the Arctic summer on board the RRS James Clark Ross, with further winter cruises completed in collaboration with the Nansen Legacy project on board the RV Helmer Hanssen. Shipboard data collection included the deployment of conductivity-temperature-depth (CTD) packages, ocean seagliders, mulitcorers, grabs, nets, trawls, and a shelf underwater camera system. The CAO programme aims to understand the changes in Arctic marine ecosystem in a quantifiable way, enabling computer models to help predict the consequences of these changes on, for example; surface ocean productivity; species distributions; food webs; and ecosystems, and the services they provide (ecosystem services). It was initially a Natural Environment Research Council (NERC) funded programme comprising four projects: Arctic PRIZE (Arctic productivity in the seasonal ice zone), led by Finlo Cottier (Scottish Association for Marine Science - SAMS); ARISE (Can we detect changes in Arctic ecosystems?), led by Claire Mahaffey (University of Liverpool); ChAOS (The Changing Arctic Ocean Seafloor), led by Christian Maerz (University of Leeds) and DIAPOD (Mechanistic understanding of the role of diatoms in the success of the Arctic Calanus complex and implications for a warmer Arctic), led by David Pond (University of Stirling). Additional projects were added to the programme in July 2018 through funding provided by NERC and the German Federal Ministry of Education and Research (BMBF). The majority of data are held by the British Oceanographic Data Centre (BODC) but a proportion of the data, primarily biological, are stored at the British Antarctic Survey Polar Data Centre (polardatacentre@bas.ac.uk) and any BMBF funded data are held by Pangaea (https://www.pangaea.de/).

The Detroit early childhood interactive map contains data relating to early childhood and education. It is meant to help stakeholders better understand the early childhood landscape better.