After over two years of public reporting, the Community Profile Report County-Level dataset will no longer be produced and distributed after the end of the public health emergency declaration. The final release will be on May 15, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic.

Effective June 22, 2021, the Community Profile Report will only be updated twice a week, on Tuesdays and Fridays.

The Community Profile Report (CPR) – County-Level is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, under the White House COVID-19 Team. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services, the Centers for Disease Control and Prevention, the Assistant Secretary for Preparedness and Response, and the Indian Health Service).

This data table provides county-level information. It is a daily snapshot in time that focuses on recent COVID-19 outcomes in the last seven days and changes relative to the week prior. Data in this report may differ from data on state and local websites. This may be due to differences in how data were reported (e.g., date specimen obtained, or date reported for cases) or how the metrics are calculated. Historical data may be updated over time due to delayed reporting.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, housing, and demographic characteristics of the U.S. population. The ACS 5-year data profiles include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts, all counties, all places and all tracts. The Data profiles contain broad social, economic, housing, and demographic information. The data are presented as both counts and percentages. There are over 2,400 variables in this dataset.

Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Contains demographic profile information from the Australian Bureau of Statistics (ABS) 2016 Census of Population and Housing. Data has been aggregated based on the top 12 countries of birth for residents.

This data has been derived from the ABS Census TableBuilder online data tool (http://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%20TableBuilder) by Australian Bureau of Statistics, used under CC 4.0.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

The City of Bloomington contracted with National Research Center, Inc. to conduct the 2019 Bloomington Community Survey. This was the second time a scientific citywide survey had been completed covering resident opinions on service delivery satisfaction by the City of Bloomington and quality of life issues. The first was in 2017. The survey captured the responses of 610 households from a representative sample of 3,000 residents of Bloomington who were randomly selected to complete the survey. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the City of Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

Community citation profile (Example).

Context

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

Content

This dataset contains 10,000 people on LinkedIn as well as the jobs held by that person on the 1st of January 2018. It was scraped using a browser extension. A rough example of which can be found here https://chrome.google.com/webstore/detail/edna-scrape-ext/hcchbehfooacdlebbpgodbfleicooahi.

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.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This feature service contains data from the American Community Survey: 5-year Estimates Data Profiles for the greater Bozeman, MT area. The attributes come from the Demographic and Housing Estimates table (DP05). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases.For Example: Pct_BAA is equal to the percentage of the population that identifies as Black or African AmericanData DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Data Profile yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyPct: Percentage of the selected populationRace/Ethnicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesAge Group:15to24: Ages 15 to 24 years old25to34: Ages 25 to 34 years old35to44: Ages 35 to 44 years old45to54: Ages 45 to 54 years old55to64: Ages 55 to 64 years old65andover: Ages 65 and overGenderMale: Male identifyingFemale: Female identifyingDownload ACS Demographic Profile data for the greater Bozeman, MT areaAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

This document contains LiftWEC’s social acceptability dataset. The dataset consists of interview transcripts sourced from semi-structured discussions conducted by a LiftWEC researcher and a range of stakeholders relevant to the field of marine renewable energy production. Semi-structured interviews were conducted with relevant actors between September and November of 2021. A snowball sampling approach was used for the selection of actors to be interviewed. Beginning with a small population of socio-political actors, this study developed a larger sample by learning from initial participants and identifying others who were relevant to the study. Interviewees were drawn up through a process of mapping, ensuring that a variety of actors holding different roles and located in different European nations engaged with the study and provided insight. Participants were then selected based upon the likelihood of having a detailed understanding of the emerging problems confronting marine renewable energy. Informed by literature, it was decided to categorise actors within three distinct profiles; (i) socio-political actors, (ii) market actors, and (iii) community actors. Three examples of interview transcripts, one from an actor relating to each of the aforementioned profiles, are presented in this dataset. The interviews were designed from the outset to allow for the analysis of debate regarding the social acceptance of novel and emerging marine renewable energy. Interviewees were prompted to discuss their perceptions of the current challenges and opportunities facing marine renewable energy technologies and their experience of how social acceptance issues are managed, and provide recommendations for the future. To support the free development and uptake of individual opinions from a variety of stakeholders, interviews were conducted on a one-to-one basis. A semi-structured interview guideline – an example is presented in section 3 of this dataset – was developed to gather data regarding the specific research objectives of the study. The interview guidelines helped to ensure comparability across interviews, especially across different countries and contexts. The questions that are part of the guideline are open questions, i.e., interview partners did not have fixed options for answering them. This provided interviewees with the possibility of freely choosing which aspect they wanted to put an emphasis or which aspects they wanted to mention. Furthermore, semi-structured interviews enabled the interviewer to spontaneously rephrase or add questions if the answers provided by the interviewee left too much room for interpretation or were not fully clear. All interviews lasted for a duration of between 40 minutes and one hour. This study that these interviews spawned from was conducted in line with the guidelines and standards set by the Queen’s University of Belfast’s Code of Conduct and Integrity in Research and its Policy and Principles on the Ethical Approval of Research. Free and informed consent was obtained from all participants prior to the collection of data from online interviews. All interviewees were provided with a project information sheet and a consent form prior to meeting, and participants were fully briefed on what the research involves. It was also explained how anonymity and confidentiality will be achieved. Permission was also sought for the audio of the meetings to be recorded and participants were made aware of their right to withdraw within one month of data gathering without penalty. Consent was also obtained for the data to be used for research purposes and for future publication. Confidentiality, a hugely important consideration in research, was ensured at all times during the course of the research.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied

The data was extracted from the NSW BioNet - Atlas system. Specifically the data table has been extracted from the Atlas of NSW Wildlife / threatened species profile module. This is the primary data that is displayed in the our TSp Web application

http://www.environment.nsw.gov.au/threatenedspeciesapp/profile.aspx?id=10616.

We intend to complete revise the way we create, manage

and maintain K&P distributions over the next 12-18mths.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

(1) Could you please confirm following data table joining method?

[OBJECTID] in shape file "CMA_sub_regions" is joining with [objected] in sheet

"ProfileDistribution" in "ProfileDistribution.xls" file. Then, [ProfileID] in

sheet "ProfileDistribution" is joining with [ProfileID] in sheet "Profiles" in

same "ProfileDistribution.xls" file.

For example, for ACT (OBJECTID:73), I can find 39 ProfileIDs in

ProfileDistribution using OBJECTID 73. If I want to see details of those 39

species or ECs, go to sheet "Profiles" based on these 39 ProfileIDs.

(2) For sheet ProfileDistribution, What is the meaning of Occurrence and its

content class such as K, P?

'K' (Known) indicates a confirmed record within any CMA sub-region. This data

is stored as a distribution layer in the TS Profiles module of Bionet-Atlas.

'P' (Predicted) indicates that this threatened species or entity is likely to

occur in a CMA sub-region . This is an expert opinion, managed in the TS

Profiles module. We recommend you disregard all Predicted data.

(3) For sheet Profiles

 .     What is the meaning of ProfileType's class such as Other, 

Population, SpeciesCode?

 .     What is the meaning of SpeciesType's class such as FA, FL, FU?

"ProfileType" attributes the type of profiles: SpeciesCode = threatened

species; Populations = threatened populations; Other = threatened

ecological community, or Threatening process as Other. Simply ameans of

systematicly separating off species and population distributions.

"SpeciesType" is to indicate whether the threatened species or population

profile is Flora (FL), Fauna (FA) or Fungi (FU).

(4) For shape file "CMA_sub_regions" We are looking for a unique and

meaningful name for the smallest spatial unit. Is [DISPLAY2] such a name?

"DISPLAY2" that would be the shortest, unique name.

(5) I only found Threatened Ecological Communities under field "GeneralType"

in "Profiles". Could you please let me know where I can find the indicator

for Threatened Species?

Your interpretation of TEC indicator is correct. Threatened Species are

indicated in Profiles by ProfileType = SpeciesCode.

Dataset Citation

NSW Office of Environment and Heritage (2013) Spatial Threatened Species and Communities (TESC) NSW 20131129. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/a6664894-1489-46d1-a6ca-16f9ab519a28.

In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16

http://msystems.asm.org/content/1/5/e00095-16

Kaggle Card: FHIR Profiles-Resources JSON File Overview Fast Healthcare Interoperability Resources (FHIR, pronounced "fire") is a standard developed by Health Level Seven International (HL7) for transferring electronic health records. The FHIR Profiles-Resources JSON file is an essential part of this standard. It provides a schema that defines the structure of FHIR resource types, including their properties and attributes.

Dataset Structure This file is structured in the JSON format, known for its versatility and human-readable nature. Each JSON object corresponds to a unique FHIR resource type, outlining its structure and providing a blueprint for the properties and attributes each resource type should contain.

Fields Description While the precise properties and attributes differ for each FHIR resource type, the typical elements you may encounter in this file include:

Id: The unique identifier for the resource type. Url: A global identifier URI for the resource type. Version: The business version of the resource. Name: The human-readable name for the resource type. Status: The publication status of the resource (draft, active, retired). Experimental: A boolean value indicating whether this resource type is experimental. Date: The date of the resource type's last change. Publisher: The individual or organization that published the resource type. Contact: Contact details for the publishers. Description: A natural language description of the resource type. UseContext: A list outlining the usability context for the resource type. Jurisdiction: Identifies the region/country where the resource type is defined. Purpose: An explanation of why the resource type is necessary. Element: A list defining the structure of the properties for the resource type, including data types and relationships with other resource types. Potential Use Cases Schema Validation: Use the schema to validate FHIR data and ensure it aligns with the defined structure and types for each resource. Interoperability: Facilitate the exchange of healthcare information with other FHIR-compatible systems by providing a standardized structure. Data Mapping: Utilize the schema to map data from other formats into the FHIR format, or vice versa. System Design: Aid the design and development of healthcare systems by offering a template for data structure.

In vivo chlorophyll a fluorescence, a proxy of chlorophyll a concentration, is one of the most frequently measured biogeochemical property in the ocean. Thousands of profiles are available from historical databases and the integration of fluorescence sensors to autonomous platforms led to a significant increase of chlorophyll fluorescence profiles acquisition. To date, benefits of such numerous data available have not yet been included in global analysis. A total of 268,184 raw chlorophyll fluorescence profiles were collected and subjected to a 10-steps quality control procedure (see supplementary literature publication). The present data product was generated from the remaining 48,600 chlorophyll fluorescence profiles. These were inter-calibrated, converted to total chlorophyll a concentration and phytoplankton community composition (i.e. microphytoplankton, nanophytoplankton and picophytoplankton) using the FLAVOR method (see further details). The data span a time period of 1958-2015, with observations from all oceanic basins and all seasons, and with depths ranging from the surface to a median sampling maximum depth of around 700m. The present data product was obtained by modelling phytoplankton biomass and composition from in situ fluorescence profiles and therefore, individual profiles should NOT BE USED as discrete observations. The correct use of the present data product is to investigate regional or temporal trends, for example to improve the open ocean climatologies of chlorophyll a concentration. This data product is intended as a living data set, with the expectation to retrieve and model additional in situ chlorophyll fluorescence profiles, especially from autonomous acquisition platforms.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 2,612 observations with 37 variables; Profiles Layer Field = 6,150 observations with 64 variables; Profiles Layer Lab= 4,575 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The Cascape program has conducted several studies, including soil surveys and mappings in AGP weredas in Tigray, Amhara, Oromia,and SNNPR in Ethiopia. The program (then Cascape project) as a collaborator of MoA/ATA has produced a map-database and soildataset of the major soil types (at 250-m resolution) of the landscapes of the 30 Cascape intervention-AGP weredas studied in 2013-2015: 5 of Tigray, 5 of Amhara, 15 of Oromia, and 5 of SNNPR.

Reference: Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 2,612 observations with 37 variables; Profiles Layer Field = 6,150 observations with 64 variables; Profiles Layer Lab= 4,575 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The Cascape program has conducted several studies, including soil surveys and mappings in AGP weredas in Tigray, Amhara, Oromia,and SNNPR in Ethiopia. The program (then Cascape project) as a collaborator of MoA/ATA has produced a map-database and soildataset of the major soil types (at 250-m resolution) of the landscapes of the 30 Cascape intervention-AGP weredas studied in 2013-2015: 5 of Tigray, 5 of Amhara, 15 of Oromia, and 5 of SNNPR.

Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. https://hdl.handle.net/10568/110868 Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b. TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen,

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

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

Additional objectives subsequently included for MCS were:

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

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

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

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

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

Polygenic Indices
Polygenic indices are available under Special Licence SN 9437. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

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

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

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

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

Secure Access versions of the MCS include:

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

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

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

SN 8785 - Millennium Cohort Study: Age 5, Sweep 3, 2006: Foundation Stage Profile and Teacher Survey
The Foundation Stage Profile data were collected as part of the Age 5, Sweep 3 survey. The variables of the evaluation of child’s development (e.g. reading, writing) contain linked data to educational records (Foundation Stage Profile for England) and teacher survey that included similar questions to the Foundation Stage Profile (survey responses for Wales, Scotland and Northern Ireland).

This data represents the indicative known and predicted distributions of threatened ecological communities, population and species. These data are a snapshot of data held and maintained in the Bionet – Threatened Species Profiles. The data were extracted mid-November 2013.\r \r The base geometry is derived from a GIS intersection of a NSW Catchment management Authority Layer and IBRA Subregions layer (Interim Bio-regionalisation of Australia). For each NSW (TSC Act) and Cwth (EPBC Act) listed entity the "known" or "predicted" occurrence of each entity is attributed against the base polygon layer based. "Prediction" of occurrence should be treated as having a low confidence.\r \r Attribution of "Known" occurrence is based on the existence of at lease one valid observation record for that polygon (locality). Validation of TS records is completed by nominated Threatened Species experts within NSW OEH (Office of Environment and Heritage). The Assignment is based on expert knowledge and is generally not assisted by distribution modelling approaches.\r \r These data are rendered live from BioNet database to the Office of Environment and Heritage Threatened Species Web site (http://www.environment.nsw.gov.au/threatenedSpeciesApp/). See the following link for an example of a profile with indicative distribution map: http://www.environment.nsw.gov.au/threatenedspeciesapp/profile.aspx?id=10616\r \r These web pages provide a view of the most current indicative distribution data. Users are recommended to check the currency of this product be for use. The data are indicative only and should be used with care - please refer to the readme and Q&A file for further information.