This template is for recording genome data from the NimbleGen platform. This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

Gene Expression Omnibus is a public functional genomics data repository supporting MIAME-compliant submissions of array- and sequence-based data. Tools are provided to help users query and download experiments and curated gene expression profiles.

We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 62% to field-wide reproducibility, based on the types of files submitted to GEO.

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series

- output/publications.csv, publication info of NCBI GEO series

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, NCBI SRA sequencing run metadata

- suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

Gene Expression Omnibus. GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. The GEO DataSets database stores original submitter-supplied records (Series, Samples and Platforms) as well as curated DataSets.

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

- Fixed some missing RAW files, that had failed to download.

- This release includes GEO series up to Dec-31, 2020;

- Fixed xlrd missing optional dependency, which affected import of some xls files, previously we were using only openpyxl (thanks to anonymous reviewer);

- All files in supplementary _RAW.tar files were checked for p values, previously _RAW.tar files were completely omitted, alas (thanks to anonymous reviewer).

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series

- output/publications.csv, publication info of NCBI GEO series

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, NCBI SRA sequencing run metadata

- suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.

Modern humans spend most of their time having eaten recently. The purpose of the current project is to understand how the blood, which contains immune cells, responds in the hours after eating a meal that is moderately high in fat. We used a sequencing method to observe the expression of all the genes in blood cells in five participants who were each fed a high fat meal on three separate days. The results are reported in the manuscript, “Temporal changes in postprandial blood transcriptomes reveal subject-specific pattern of expression of innate immunity genes after a high-fat meal." We used a sequencing method to observe the expression of all the genes in blood cells in five participants who were each fed a high fat meal on three separate days, resulting in 45 whole blood transcriptomes. For each sample, 3 mL of venous whole blood was drawn into a Tempus Blood RNA tube, shaken vigorously, and then frozen at -80°C until use. Total RNA was purified with the Tempus Spin RNA Isolation Kit with minor modifications to the manufacturer’s protocol. To remove residual genomic DNA, RNA samples were treated on-column with RNase-Free DNase per manufacturer’s instructions. RNA quantity, quality, and integrity were assessed with NanoDrop 1000 and 2100 Bioanalyzer. All isolated RNA had A260/A280 ratios greater than 2 and RNA integrity numbers higher than 7.3. RNA-Seq libraries were constructed at the DNA Technologies and Expression Core at the University of California, Davis, using the Ovation Human Blood RNA-Seq Library System (NuGEN Technologies). Sequencing was performed in a 2x100bp format with 45 samples multiplexed on 3 lanes on an Illumina HiSeq 4000. Analysis of the data is reported in the manuscript, “Temporal changes in postprandial blood transcriptomes reveal subject-specific pattern of expression of innate immunity genes after a high-fat meal.”

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

Mineral, Temperature, Gravity, and Fault Density maps in the Coso Geothermal Field in California.

Project activities geographical data submitted by grantees as part of the final report submission for grants awarded in FY17 and before. This is extracted via the old GMS database system.

AbstractFunding agencies are reluctant to support data archiving, even though large research funders such as the National Science Foundation (NSF) and the National Institutes of Health acknowledge its importance for scientific progress. Our quantitative estimates of data reuse indicate that ongoing financial investment in data-archiving infrastructure provides a high scientific return., Usage notesPubMed Central reuse of GEO datasets deposited in 2007This is the raw data behind the analysis. It contains one row for every mention of a 2007 GEO dataset in PubMed Central. Each row identifies the mentioned GEO dataset, the PubMed Central article that mentions the dataset's accession number, whether the authors of the dataset and the attributing article overlap, and whether this is considered an instance of third-party data reuse.PMC_reuse_of_2007_GEO_datasets.csvAggregate Table DataAggregate table data behind the figures and results in the README associated with the main dataset. Includes Baseline metrics used for extrapolating PubMed Central (PMC) results to PubMed, Number of mentions of a 2007 GEO dataset by authors who submitted the dataset, and Number of mentions of a dataset by authors who DID NOT submit the dataset across 2007-2010.tables.csv

Uploaded datasets are detailed exposure information (chemical concentrations and water quality parameters) for exposures conducted in a flow through diluter system with larval Pimephales promelas to four different pyrethroid pesticides. The GEO submission URL links to the NCBI GEO database and contains gene expression data from whole larvae exposed to different concentrations of the pyrethroids across multiple experiments. This dataset is associated with the following publication: Biales, A., M. Kostich, A. Batt, M. See, R. Flick, D. Gordon, J. Lazorchak, and D. Bencic. Initial Development of a Multigene Omics-Based Exposure Biomarker for Pyrethroid Pesticides. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 179(0): 27-35, (2016).

Data from : ‘Stability of genome-wide methylation patterns and parental environmental effects in the widespread, long-lived Lombardy poplar’

An Vanden Broeck*, Tim Meese*, Pieter Verschelde, Karen Cox, Berthold Heinze, Dieter Deforce, Ellen De Meester and Filip Van Nieuwerburgh

* These authors contributed equally.

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Background: Despite the increasing number of epigenomic studies in plants, little is known about the forces that shape the methylome in long-lived woody perennials. The Lombardy poplar (Populus nigra cv. 'Italica' Duroi) offers an ideal opportunity to investigate the impact of the individual environmental history of trees on the methylome.

Results: We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH-context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe any biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring’s’ phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment.

Conclusions: Genome-wide cytosine methylation patterns in symmetrical GC-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in GC-context can be used as bio-markers to infer a common ancestor and thus to investigate the environmental history of a specific Lombardy poplar on short time-scales. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world.

ADDITIONAL FILES

1. Additional file 1. CSV-file with information on the Lombardy poplar trees samples used for whole genome bisulfite sequencing (WGBS) in the two methylome experiments (metadata). The raw fastq datafiles obtained by whole genome bisulfite sequencing (WGBS) are available at the Gene Expression Omnibus (GEO) database (submission GSE225596).

2. Additional file 2. Zip-folder with html-files of the mapping and methylation statistics of the genomes of the 16 individual Lombardy poplar samples for the three sequence contexts (CpG, CHG, CHH) (bismark reports) (processed data).

3. Additional file 3. Zip-folder with: i) excel-files listing the genes in DMRs, and ii) PNG-files with the ‘Biological Coefficient of Variation (BCV)’-plots between any of the six pairwise comparisons of Lombardy poplars grouped per ortet and identified with Bioconductor package edgeR. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).

4. Additional file 4. EXCEL-file with the total list of GO terms that were enriched in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).

5. Additional file 5. Zip-folder with PNG-files representing heatmaps and excel-files with clustered GO terms significant over-represented in promoters and gene regions located in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively. The files were obtained with the Bioconductor package simplifyEnrichment (processed data).

6. Additional file 6. CSV-file with the raw data of the bud set observations in the common garden experiment (raw data).

7. Additional file 7. HTML-file with the R source codes to reproduce the results of the bud set analysis (code, R script).

8. Additional file 8. A text-file representing the Snakefile (i.e. a readable Python-based workflow) including the different steps and rules of the bioinformatics of the WGBS data analyses (code, Snakefile).

9. Additional file 9. RMD-file with the code to reproduce the analyses to identify differential methylated predefined regions (code, R script).

10. Additional file 10. includes the R-script with the code to reproduce the clustering and visualizing of the GO enrichment results (code, R script).

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To understand the diversity of expression states in IDH-wildtype Glioblastomas, we profiled 24,131 single cells from 28 patients with GBM by single-cell RNA sequencing (7,930 cells by Smartseq2 and 16,201 by 10X). Tumors were disaggregated, sorted into single cells, and profiled by Smart-seq2 (main text) or 10X (supplementary text).--------------------------------------------------------------Authors state "We have difficulties in approving raw data submission to GEO or dbGAP and are therefore submitting at this point only the processed data and the metadata (both attached) and will continue in parallel with the process of granting approval for raw data submission in dbGAP and another repository (DUOS). "

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g., 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. PAD-US provides a full inventory geodatabase, spatial analysis, statistics, data downloads, web services, poster maps, and data submissions included in efforts to track global progress toward biodiversity protection. PAD-US integrates spatial data to ensure public lands and other protected areas from all jurisdictions are represented. PAD-US version 4.0 includes new and updated data from the following data providers. All other data were transferred from previous versions of PAD-US. Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in regular PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. Revisions associated with the federal estate in this version include updates to the Federal estate (fee ownership parcels, easement interest, management designations, and proclamation boundaries), with authoritative data from 7 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), and the U.S. Forest Service (USFS). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup/ ). This includes improved the representation of boundaries and attributes for the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. Additionally, National Cemetery boundaries were added using geospatial boundary data provided by the U.S. Department of Veterans Affairs and NASA boundaries were added using data contained in the USGS National Boundary Dataset (NBD). State Updates - USGS is committed to building capacity in the state data steward network and the PAD-US Team to increase the frequency of state land and NGO partner updates, as resources allow. State Lands Workgroup ( https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/state-lands-workgroup ) is focused on improving protected land inventories in PAD-US, increase update efficiency, and facilitate local review. PAD-US 4.0 included updates and additions from the following seventeen states and territories: California (state, local, and nonprofit fee); Colorado (state, local, and nonprofit fee and easement); Georgia (state and local fee); Kentucky (state, local, and nonprofit fee and easement); Maine (state, local, and nonprofit fee and easement); Montana (state, local, and nonprofit fee); Nebraska (state fee); New Jersey (state, local, and nonprofit fee and easement); New York (state, local, and nonprofit fee and easement); North Carolina (state, local, and nonprofit fee); Pennsylvania (state, local, and nonprofit fee and easement); Puerto Rico (territory fee); Tennessee (land trust fee); Texas (state, local, and nonprofit fee); Virginia (state, local, and nonprofit fee); West Virginia (state, local, and nonprofit fee); and Wisconsin (state fee data). Additionally, the following datasets were incorporated from NGO data partners: Trust for Public Land (TPL) Parkserve (new fee and easement data); The Nature Conservancy (TNC) Lands (fee owned by TNC); TNC Northeast Secured Areas; Ducks Unlimited (land trust fee); and the National Conservation Easement Database (NCED). All state and NGO easement submissions are provided to NCED. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history/ for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B 9) Revised - April 2024 (Version 4.0) https://doi.org/10.5066/P96WBCHS Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Funding agencies are reluctant to support data archiving, even though large research funders such as the National Science Foundation (NSF) and the National Institutes of Health acknowledge its importance for scientific progress. Our quantitative estimates of data reuse indicate that ongoing financial investment in data-archiving infrastructure provides a high scientific return.

This FAIRsharing record describes: The Information for Authors page contains a broad range of guidelines for publishing in The Lancet. With regards to data deposition, novel gene sequences should be deposited in a public database (GenBank, EMBL, or DDBJ), and the accession number provided. Authors of microarray papers should include in their submission the information recommended by the MIAME guidelines. Authors should also submit their experimental details to one of the publicly available databases: ArrayExpress or GEO.

URL: https://geoscience.data.qld.gov.au/dataset/ds000001

This practice direction forms part of the Queensland Resources Reporting Lodgement, GSQ Open Data Portal, Reporting Guideline 2020 to assist industry with spatial data lodgement.

The purpose is to outline in greater detail, formatting and content requirements to ensure the standardisation of spatial information and provide consistency of submissions received, enabling the department to more effectively standardise, process and integrate the data.

Submitted data must meet the standards for content and file formats as set out in this document. Lodgement will be via use of submission templates, designed to guide the user in assembling data, and to ensure consistency with the defined data formats and standards.

This practice direction has been divided into two sections covering the submission of spatial information as digital spatial data and digital maps.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means.

The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g., 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. PAD-US provides a full inventory geodatabase, spatial analysis, statistics, data downloads, web services, poster maps, and data submissions included in efforts to track global progress toward biodiversity protection.

PAD-US integrates spatial data to ensure public lands and other protected areas from all jurisdictions are represented. PAD-US version 4.1 includes new and updated data from the following data providers. All other data were transferred from previous versions of PAD-US.

Federal updates - USGS remains committed to updating federal fee owned lands data and major designation changes in regular PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. Revisions associated with the federal estate in this version include updates to the Federal estate (fee ownership parcels, easement interest, management designations, and proclamation boundaries), with authoritative data from two agencies, the Department of Defense (DOD) and the U.S. Forest Service (USFS). Additionally, unit names were updated to reflect recent name changes to Fort Benning, Fort Bragg, and Jocelyn Nungaray National Wildlife Refuge. The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup/ ). This includes improved the representation of boundaries and attributes for the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders.

State Updates - USGS is committed to building capacity in the state data steward network and the PAD-US Team to increase the frequency of state land and NGO partner updates, as resources allow. State Lands Workgroup (SLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/state-lands-workgroup ) is focused on improving protected land inventories in PAD-US, increase update efficiency, and facilitate local review. This version includes updates and additions from the following two states: Texas (state, local, and nonprofit fee) and Arizona (state, local, and nonprofit fee). All state and NGO easement submissions are provided to NCED. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas.

For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual .

A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history/ for more information):

1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B 9) Revised - April 2024 (Version 4.0) https://doi.org/10.5066/P96WBCHS 10) Revised - March 2025 (Version 4.1) https://doi.org/10.5066/P96WBCHS

Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.