The data are maintained at the National Center for Biotechnology Information (NCBI) GEO depository https://www.ncbi.nlm.nih.gov/geo/ . There are three accession numbers (which can be entered at this site): • GSE160535 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol A superseries which links to the two separate data sets from the same experiment (below) • GSE158857 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol [non-coding small RNA] The non-coding small RNA dataset (includes microRNA and PIWI-RNA data and metadata • GSE160520 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol [mRNA] The mRNA data set including metadata. This dataset is associated with the following publication: Toth, G., J. Martinson, D. Bencic, D. Lattier, M. Kostich, and A. Biales. Development of omcis biomarkers for estrogen exposure using mRNA, miRNA and piRNAs. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 235: 105807, (2021).

The Open Government Data portals (OGD) thanks to the presence of thousands of geo-referenced datasets, containing spatial information, are of extreme interest for any analysis or process relating to the territory. For this to happen, users must be enabled to access these datasets and reuse them. An element often considered hindering the full dissemination of OGD data is the quality of their metadata. Starting from an experimental investigation conducted on over 160,000 geospatial datasets belonging to six national and international OGD portals, this work has as its first objective to provide an overview of the usage of these portals measured in terms of datasets views and downloads. Furthermore, to assess the possible influence of the quality of the metadata on the use of geospatial datasets, an assessment of the metadata for each dataset was carried out, and the correlation between these two variables was measured. The results obtained showed a significant underutilization of geospatial datasets and a generally poor quality of their metadata. Besides, a weak correlation was found between the use and quality of the metadata, not such as to assert with certainty that the latter is a determining factor of the former.

The dataset consists of six zipped CSV files, containing the collected datasets' usage data, full metadata, and computed quality values, for about 160,000 geospatial datasets belonging to the three national and three international portals considered in the study, i.e. US (catalog.data.gov), Colombia (datos.gov.co), Ireland (data.gov.ie), HDX (data.humdata.org), EUODP (data.europa.eu), and NASA (data.nasa.gov).

Data collection occurred in the period: 2019-12-19 -- 2019-12-23.

The header for each CSV file is:

[ ,portalid,id,downloaddate,metadata,overallq,qvalues,assessdate,dviews,downloads,engine,admindomain]

where for each row (a portal's dataset) the following fields are defined as follows:

portalid: portal identifier

id: dataset identifier

downloaddate: date of data collection

metadata: the overall dataset's metadata downloaded via API from the portal according to the supporting platform schema

overallq: overall quality values computed by applying the methodology presented in [1]

qvalues: json object containing the quality values computed for the 17 metrics presented in [1]

assessdate: date of quality assessment

dviews: number of total views for the dataset

downloads: number of total downloads for the dataset (made available only by the Colombia, HDX, and NASA portals)

engine: identifier of the supporting portal platform: 1(CKAN), 2 (Socrata)

admindomain: 1 (national), 2 (international)

[1] Neumaier, S.; Umbrich, J.; Polleres, A. Automated Quality Assessment of Metadata Across Open Data Portals.J. Data and Information Quality2016,8, 2:1–2:29. doi:10.1145/2964909

Sequences from this study are available at the NCBI GEO under accession series GSE131846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE131846

This proposal addressed the theme of “impact of oil spills on public health†. Specifically, the proposal addressed the general hypothesis, which is: upon oil/dispersant respiratory exposure there will be a higher carcinogenic potential of lung tissue.

To test this hypothesis, we profiled and confirmed the existence of molecular signatures of carcinogenesis through RNA-seq analysis of a mouse model treated with instilled oil/dispersants. We exposed the wild-type C57BL/6 (B6) mice to BP crude oil, dispersant 9500, dispersant 9527, oil + 9527, oil + 9500 and H2O (as control) using intratracheal instillation method for 2 weeks. We then performed RNA-seq analysis of the lung tissue from the mice to identify differentially expressed (DEx) genes (DEGs) in the treated mice vs. the control mice. These DEGs were functionally annotated to search for GO terms and pathways related to carcinogenesis.

For each treatment group, 3 male and 3 female mice were used. Therefore, we generated RNA-seq data for a total of 36 animals (6 animals/group x 6 treatment group).

We have submitted the RNA-seq data to NCBI's GEO (Gene Expression Omnibus) online database (https://www.ncbi.nlm.nih.gov/geo/). The dataset is now assigned a GEO series number GSE137204.

In the GEO website, RNA-seq data are organized under three types: the metadata, the processed data and the raw data files. The metadata describes the treatment group and other information related to a sample. The processed data files contain raw counts of sequencing reads for transcripts. The raw data files are the raw fastq data files generated in the RNA-seq experiments.

This dataset supports the publication: Liu, Yao-Zhong; Charles A. Miller; Yan Zhuang; Sudurika S. Mukhopadhyay; Shigeki Saito; Edward B. Overton; and Gilbert F. Morris. 2020. The Impact of the Deepwater Horizon Oil Spill upon Lung Health—Mouse Model-Based RNA-Seq Analyses. International Journal of Environmental Research and Public Health, 17(15), 5466. doi:10.3390/ijerph17155466

Summary

This metadata record provides details of the data supporting the claims of the related manuscript: “SOX4 and SMARCA4 cooperatively regulate PI3K signaling through transcriptional activation of TGFBR2”.

The related study aimed to demonstrate that the transcription factor SOX4 is a key regulator of PI3K signalling in triple-negative breast cancers (TNBCs).

Type of data: RNA sequencing data (n=1,031) from human tumors; Illumina HT-29 v3 expression data for the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) project (n=1,992)

Subject of data: human breast tumors and breast cancer cell lines

Data access

RNAseq data have been deposited in the Gene Expression Omnibus (GEO) under accession number https://identifiers.org/geo:GSE158295. Proteomic data from MIB/MS (https://identifiers.org/pride.project:PXD022596) and IP-MS (https://identifiers.org/pride.project:PXD022811) analyses have been made available through the PRIDE database. SMARCA4 (https://identifiers.org/geo:GSE72141), H3K27ac and H3K4me3 (https://identifiers.org/geo:GSE85158) ChIP-Seq raw data from MDA-MB-231 cells were acquired from Gene Expression Omnibus (GEO). The data for Figures 1, 2, and 5 are presented in supplemental Tables S1, S2, S3, S4, and S5, which are available in Excel format as part of this metadata record, as well as in PDF format via the supplementary materials of the related article.

Corresponding author(s) for this study

Michael L. Gatza, Ph.D., Rutgers Cancer Institute of New Jersey, 195 Little Albany Street CINJ 4558, New Brunswick NJ 08903, Ph: 732-235-8751, Michael.gatza@cinj.rutgers.edu

The National Oceanographic Data Committee (NODC) of the Netherlands is the national platform for exchange of oceanographic and marine data and information, and for advisory services in the field of ocean and marine data management. The overall objective of the NODC is to effect a major and significant improvement in the overview and access to marine and oceanographic data and data-products from government and research institutes in the Netherlands. This is not done alone and only with a national focus, but on a European scale as an active partner in the Pan-European SeaDataNet project, complying to the INSPIRE and the new Marine Strategy EU Directives, and on a global scale as the Netherlands representative in major international organisations in this field, ICES and IOC-IODE. A major step has been made with the launch of the NODCi - National Infrastructure for access to Oceanographic and Marine Data and Information. This was developed in the framework of the Ruimte voor Geo-Informatie (RGI) programme as RGI-014 project. It includes a new NODC-i portal (www.nodc.nl), that provides users with a range of metadata services and a unique interface to the data management systems of each of the NODC members. By this Common Data Index (CDI) interface, users can get harmonised access to the datasets, that are managed in a distributed way at each of the NODC members. The NODCi portal functions as the Dutch node in the SeaDataNet infrastructure. The NODC CDI service contains several thousands of references to individual marine and oceanographic datasets. For inclusion in the National Geo Register these have been aggregated by combinations of Data Holding Centres - Disciplines. Each NGR - NODC record therefore represents a large number of individual metadata records and associated datasets. By following the specified URL to the NODCi portal, users can consider these metadata in detail and can achieve downloading of interesting datasets via the shopping cart transaction system, that is integrated in the NODCi portal.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

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.

This file contains locality and relative to height datum measurements of groundwater (RSWL) from the GAB (JK) Aquifer that were used to develop the corrected potentiometric surface for the GAB Aquifer. Also contained in the datafile are unit numbers and state of origin for relevant wells.

Purpose

To indicate the corrected potentiometric surface for the GAB Aquifer.

Dataset History

This file was developed along with a number of other data sets as part of the Australian Government funded research program entitled Allocating Water and Maintaining Springs in the GAB.

http://archive.nwc.gov.au/library/topic/groundwater/allocating-water-and-maintaining-springs-in-the-great-artesian-basin

This file contains locality and relative to height datum measurements of groundwater (RSWL) from the GAB (JK) Aquifer that were used to develop the corrected potentiometric surface for the GAB Aquifer. Also contained in the datafile are unit numbers and state of origin for relevant wells. Data for this file were extracted from the SA Government-maintained Geo database SA_GEODATA.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) RSWL Data Point - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/81f542a2-e4de-455c-9dfa-4e0bae84598d.

2010 US Census Block Groups for Hennepin County with subset of PL94-171 demographic data. The Hennepin County GIS Office downloaded US Census data from the following sites:Shapefiles:http://www.census.gov/cgi-bin/geo/shapefiles2010/main PL94-171:http://www.census.gov/rdo/data/2010_census_redistricting_data_pl_94-171_summary_files.html PL94-171 tabular data was post processed per instructions and stored in an Access database. Records and a subset of the 290 plus fields were extracted from the master tables using SQL statements. A copy of the statement has been included in the Lineage section of metadata. Tables were registered as a geodatabase and copied to a File Geodatabase. Shapefiles were imported into the File Geodatabase and projected to UTM Zone 15 N. The feature classes were joined to the tabular data and saved as the final US Census layer.The feature classes underwent visual inspection. The number of records were compared and checked. The attribute values were compared to existing Maptitude US Census data. Please contact the Hennepin GIS Office if you require additional PL94-171 fields. Hennepin County GIS Office A-705 Government Center Minneapolis, Minnesota 55487-075 GIS.Info@co.hennepin.mn.us Phone: 612-596-9484 FAX: 612-348-2837 The original metadata is contained below. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

Link to Attribute Table Information: http://gis.hennepin.us/OpenData/Metadata/2010%20Census%20Block%20Groups.pdf

Use Limitations: This data (i) is furnished "AS IS" with no representation as to completeness or accuracy; (ii) is furnished with no warranty of any kind; and (iii) is not suitable for legal, engineering or surveying purposes. Hennepin County shall not be liable for any damage, injury or loss resulting from this data.

© US Census Bureau. See additional information in Abstract and Use Limitations. This data was modified by the Hennepin County GIS Office. This layer is a component of Datasets for Hennepin County AGOL and Hennepin County Open Data..

The National Oceanographic Data Committee (NODC) of the Netherlands is the national platform for exchange of oceanographic and marine data and information, and for advisory services in the field of ocean and marine data management. The overall objective of the NODC is to effect a major and significant improvement in the overview and access to marine and oceanographic data and data-products from government and research institutes in the Netherlands. This is not done alone and only with a national focus, but on a European scale as an active partner in the Pan-European SeaDataNet project, complying to the INSPIRE and the new Marine Strategy EU Directives, and on a global scale as the Netherlands representative in major international organisations in this field, ICES and IOC-IODE. A major step has been made with the launch of the NODCi - National Infrastructure for access to Oceanographic and Marine Data and Information. This was developed in the framework of the Ruimte voor Geo-Informatie (RGI) programme as RGI-014 project. It includes a new NODC-i portal (www.nodc.nl), that provides users with a range of metadata services and a unique interface to the data management systems of each of the NODC members. By this Common Data Index (CDI) interface, users can get harmonised access to the datasets, that are managed in a distributed way at each of the NODC members. The NODCi portal functions as the Dutch node in the SeaDataNet infrastructure. The NODC CDI service contains several thousands of references to individual marine and oceanographic datasets. For inclusion in the National Geo Register these have been aggregated by combinations of Data Holding Centres - Disciplines. Each NGR - NODC record therefore represents a large number of individual metadata records and associated datasets. By following the specified URL to the NODCi portal, users can consider these metadata in detail and can achieve downloading of interesting datasets via the shopping cart transaction system, that is integrated in the NODCi portal.

The table shows, for each agricultural establishment in the public and private sectors, an estimate of the number of meals taken per day and per year in the establishment from the population. The staff figures correspond to the 2018-2019 school year and are derived from the DeciEA information system (Decision of Agricultural Education) of the Directorate-General for Education and Research (DGER) of the Ministry of Agriculture and Food.

This data can be associated with the location data of agricultural educational institutions. (Last update 2019 — available at http://catalogue.geo-ide.developpement-durable.gouv.fr/catalogue/srv/fre/catalog.search#/metadata/fr-120066022-jdd-690f1f7e-7876-4f2b-84e5-08c219abe58c)

The National Oceanographic Data Committee (NODC) of the Netherlands is the national platform for exchange of oceanographic and marine data and information, and for advisory services in the field of ocean and marine data management. The overall objective of the NODC is to effect a major and significant improvement in the overview and access to marine and oceanographic data and data-products from government and research institutes in the Netherlands. This is not done alone and only with a national focus, but on a European scale as an active partner in the Pan-European SeaDataNet project, complying to the INSPIRE and the new Marine Strategy EU Directives, and on a global scale as the Netherlands representative in major international organisations in this field, ICES and IOC-IODE. A major step has been made with the launch of the NODCi - National Infrastructure for access to Oceanographic and Marine Data and Information. This was developed in the framework of the Ruimte voor Geo-Informatie (RGI) programme as RGI-014 project. It includes a new NODC-i portal (www.nodc.nl), that provides users with a range of metadata services and a unique interface to the data management systems of each of the NODC members. By this Common Data Index (CDI) interface, users can get harmonised access to the datasets, that are managed in a distributed way at each of the NODC members. The NODCi portal functions as the Dutch node in the SeaDataNet infrastructure. The NODC CDI service contains several thousands of references to individual marine and oceanographic datasets. For inclusion in the National Geo Register these have been aggregated by combinations of Data Holding Centres - Disciplines. Each NGR - NODC record therefore represents a large number of individual metadata records and associated datasets. By following the specified URL to the NODCi portal, users can consider these metadata in detail and can achieve downloading of interesting datasets via the shopping cart transaction system, that is integrated in the NODCi portal.

The official tax maps for the City of New York are maintained by the DEPARTMENT OF FINANCE Tax Map Office. Tax maps show the lot lines, the block and lot numbers, the street names, lot dimensions, and easements. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_DigitalTaxMap.md

description: GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C. Corton, R. Ugarte, and M. Antoniou. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 108: 30-42, (2017).; abstract: GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C. Corton, R. Ugarte, and M. Antoniou. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 108: 30-42, (2017).

Description:

This dataset contains regional country boundaries around the SAFE project from two online sources.

Digital chart of the world from https://worldmap.harvard.edu/data/geonode:Digital_Chart_of_the_World.

A Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG). This is a large vector GIS database containing shorelines for seas and lakes and other vector data layers at a number of different resolutions. The full archive was downloaded from: < href='http://www.soest.hawaii.edu/pwessel/gshhg/'>http://www.soest.hawaii.edu/pwessel/gshhg/

Details of the file extraction and coverage can be found here: https://www.safeproject.net/dokuwiki/safe_gis/country_shoreline.

Project: This dataset was collected as part of the following SAFE research project: SAFE CORE DATA

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This dataset consists of 2 files: SAFE_Country_border_metadata.xlsx, SAFE_shore_and_country_borders.zip

SAFE_Country_border_metadata.xlsx

This file only contains metadata for the files below

SAFE_shore_and_country_borders.zip

Description: Contains three shapefiles of local country borders

This file contains 3 data tables:

1. Feature properties (described in worksheet SAFE_Regional_DCW)

   Description: Field descriptions for shapefile properties

   Number of fields: 13

   Number of data rows: Unavailable (table metadata description only).

   Fields:

   • ID: Feature ID (Field type: id)
   • FIPS10_4: FIPS country code (Field type: categorical)
   • COUNTRY: Country name (Field type: categorical)
   • CONTCODE: Continent code (Field type: categorical)
   • CONT: Continent name (Field type: categorical)
   • F_CODE: Geo code (Field type: categorical)
   • F_CODE_DES: Description of F code (Field type: comments)
   • OW_ABBREV: OW country codes (Field type: categorical)
   • ISO_A2: Two letter ISO country code (Field type: categorical)
   • ISO_A3: Three letter ISO country code (Field type: categorical)
   • VMAP_ID: Base vector map source ids (Field type: categorical)
   • FACTBK_CTY: Factbook Country name (Field type: categorical)
   • Fips2: FIPS 2 character code (Field type: categorical)

2. Feature properties (described in worksheet SAFE_Regional_GSHHS_Shoreline_f)

   Description: Field descriptions for shapefile properties

   Number of fields: 6

   Number of data rows: Unavailable (table metadata description only).

   Fields:

   • id: Feature ID (Field type: id)
   • level: Level of nesting of feature (Field type: numeric)
   • source: Source of original geodata (Field type: categorical)
   • parent_id: Unknown feature ID reference (all values = -1) (Field type: id)
   • sibling_id: Unknown feature ID reference (all values = -1) (Field type: id)
   • area: Area of feature (Field type: numeric)

3. Feature properties (described in worksheet SAFE_Regional_GSHHS_Shoreline_i)

   Description: Field descriptions for shapefile properties

   Number of fields: 6

   Number of data rows: Unavailable (table metadata description only).

   Fields:

   • id: Feature ID (Field type: id)
   • level: Level of nesting of feature (Field type: numeric)
   • source: Source of original geodata (Field type: categorical)
   • parent_id: Unknown feature ID reference (all values = -1) (Field type: id)
   • sibling_id: Unknown feature ID reference (all values = -1) (Field type: id)
   • area: Area of feature (Field type: numeric)

Date range: 2010-10-01 to 2019-10-01

Latitudinal extent: -11.0049 to 21.1200

Longitudinal extent: 95.0000 to 141.0200

The National Oceanographic Data Committee (NODC) of the Netherlands is the national platform for exchange of oceanographic and marine data and information, and for advisory services in the field of ocean and marine data management. The overall objective of the NODC is to effect a major and significant improvement in the overview and access to marine and oceanographic data and data-products from government and research institutes in the Netherlands. This is not done alone and only with a national focus, but on a European scale as an active partner in the Pan-European SeaDataNet project, complying to the INSPIRE and the new Marine Strategy EU Directives, and on a global scale as the Netherlands representative in major international organisations in this field, ICES and IOC-IODE. A major step has been made with the launch of the NODCi - National Infrastructure for access to Oceanographic and Marine Data and Information. This was developed in the framework of the Ruimte voor Geo-Informatie (RGI) programme as RGI-014 project. It includes a new NODC-i portal (www.nodc.nl), that provides users with a range of metadata services and a unique interface to the data management systems of each of the NODC members. By this Common Data Index (CDI) interface, users can get harmonised access to the datasets, that are managed in a distributed way at each of the NODC members. The NODCi portal functions as the Dutch node in the SeaDataNet infrastructure. The NODC CDI service contains several thousands of references to individual marine and oceanographic datasets. For inclusion in the National Geo Register these have been aggregated by combinations of Data Holding Centres - Disciplines. Each NGR - NODC record therefore represents a large number of individual metadata records and associated datasets. By following the specified URL to the NODCi portal, users can consider these metadata in detail and can achieve downloading of interesting datasets via the shopping cart transaction system, that is integrated in the NODCi portal.

OBS! This is the limma results of the analysis. See https://doi.org/10.5281/zenodo.8162229 for the DESeq2/DEXSeq results.

This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (article).
All results were computed using the R package pairedGSEA, which utilized Limma (Ritchie et al., 2015) and fgsea (Korotkevich et al., 2019).

Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.

The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".

All datasets were obtained from a local copy of the ARCHS4 v11 database of transcript counts (Lachmann et al., 2018).

The National Oceanographic Data Committee (NODC) of the Netherlands is the national platform for exchange of oceanographic and marine data and information, and for advisory services in the field of ocean and marine data management. The overall objective of the NODC is to effect a major and significant improvement in the overview and access to marine and oceanographic data and data-products from government and research institutes in the Netherlands. This is not done alone and only with a national focus, but on a European scale as an active partner in the Pan-European SeaDataNet project, complying to the INSPIRE and the new Marine Strategy EU Directives, and on a global scale as the Netherlands representative in major international organisations in this field, ICES and IOC-IODE. A major step has been made with the launch of the NODCi - National Infrastructure for access to Oceanographic and Marine Data and Information. This was developed in the framework of the Ruimte voor Geo-Informatie (RGI) programme as RGI-014 project. It includes a new NODC-i portal (www.nodc.nl), that provides users with a range of metadata services and a unique interface to the data management systems of each of the NODC members. By this Common Data Index (CDI) interface, users can get harmonised access to the datasets, that are managed in a distributed way at each of the NODC members. The NODCi portal functions as the Dutch node in the SeaDataNet infrastructure. The NODC CDI service contains several thousands of references to individual marine and oceanographic datasets. For inclusion in the National Geo Register these have been aggregated by combinations of Data Holding Centres - Disciplines. Each NGR - NODC record therefore represents a large number of individual metadata records and associated datasets. By following the specified URL to the NODCi portal, users can consider these metadata in detail and can achieve downloading of interesting datasets via the shopping cart transaction system, that is integrated in the NODCi portal.

These data can be used in a geographic information system (GIS) for any number of purposes such as assessing wildlife habitat, water quality, pesticide runoff, land use change, etc. The State data sets are provided with a 300 meter buffer beyond the State border to facilitate combining the State files into larger regions. The user must have a firm understanding of how the datasets were compiled and the resulting limitations of these data. The National Land Cover Dataset was compiled from Landsat satellite TM imagery (circa 1992) with a spatial resolution of 30 meters and supplemented by various ancillary data (where available). The analysis and interpretation of the satellite imagery was conducted using very large, sometimes multi-state image mosaics (i.e. up to 18 Landsat scenes). Using a relatively small number of aerial photographs for 'ground truth', the thematic interpretations were necessarily conducted from a spatially-broad perspective. Furthermore, the accuracy assessments (see below) correspond to 'federal regions' which are groupings of contiguous States. Thus, the reliability of the data is greatest at the State or multi-State level. The statistical accuracy of the data is known only for the region.

AbstractNational Address Points is a derived, spatial adaptation of Geoscape's Geocoded National Address File (G-NAF) product. It is a point dataset, with records representing the physical location of addresses in Australia.PurposeThis product has been developed for spatial visualisation and analysis of address points across Australia. It is optimised for visualisation through the Digital Atlas of Australia or a desktop GIS.For other uses of national address data consider using G-NAF or G-NAF Core.While G-NAF is available to the public under a Creative Commons Attribution 4.0 license, there are special restrictions relating to the use of the data for the sending of mail purposes. Further information on restrictions is available here, and in the Terms of Use below.CurrencyDate modified: May 2025Modification frequency: QuarterlyData ExtentSpatial ExtentNorth: -8° South: -45° East: 168° West: 96° Source InformationNational Address Points incorporates and was developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.Geoscience Australia is providing this data to the public under a Creative Commons Attribution 4.0 license. There are special restrictions relating to the use of the data for the sending of mail purposes. Further information on this use restriction is available here.The data was accessed through data.gov.au.Metadata: Geoscape G-NAF Metadata StatementLineage StatementNational Address Points is built from Geoscape's quarterly released G-NAF psv text files. It uses the available data model in the G-NAF product description to appropriately join the tables into a flat structure and is geocoded using the latitude and longitude attributes in GDA2020. During development, decisions have been made to include or exclude certain attributes, as well as develop new ones where suitable. These decisions maintain the integrity of the original dataset and were made considering envisioned use case(s) and access of the dataset in the Digital Atlas of Australia.Two additional attributes have been developed and included in the National Address Points dataset: COMPLETE_ADDRESS and MB_CENSUS_YEAR.COMPLETE_ADDRESS is a concatenated, complete address string of the address components (i.e. Flat attributes, Level attributes, Lot attributes, Street Number attributes, Street Name attributes, Locality, State and Postcode).MB_CENSUS_YEAR is the year of the ABS Census the Mesh Block data is extracted from. The psv file that with this information is dynamically named and attributed (with the year in the file name and attribute name). A method has been developed to ensure this is consistently and accurately populated.Attributes excluded from the final National Address Points product are still accessible to users by downloading and joining to Geoscape's original G-NAF product, found at data.gov.au.Original G-NAF attribute names, data types and data content included in National Address Points are not changed.Data DictionaryAttribute NameDescriptionADDRESS_DETAIL_PIDThe Persistent Identifier is unique to the real world feature this record represents.DATE_CREATEDDate the address record was created.DATE_LAST_MODIFIEDDate the address record was last modified.DATE_RETIREDDate the address record was retired.COMPLETE_ADDRESSA concatenated complete address string of address components (Flat, Level, Lot, Street Number, Street Name attributes, Locality, State and Postcode).ADDRESS_SITE_NAMEAddress site name.BUILDING_NAMECombines both building/property name fields.LOCATION_DESCRIPTIONA general field to capture various references to address locations alongside another physical location.FLAT_TYPESpecification of the type of a separately identifiable portion within a building/complex.FLAT_NUMBER_PREFIXFlat/unit number prefix.FLAT_NUMBERFlat/unit number.FLAT_NUMBER_SUFFIXFlat/unit number suffix.LEVEL_TYPELevel Type.LEVEL_NUMBER_PREFIXLevel number prefix.LEVEL_NUMBERLevel number.LEVEL_NUMBER_SUFFIXLevel number suffix.NUMBER_FIRST_PREFIXPrefix for the first (or only) number in range.NUMBER_FIRSTIdentifies first (or only) street number in range.NUMBER_FIRST_SUFFIXSuffix for the first (or only) number in range.NUMBER_LAST_PREFIXPrefix for the last number in range.NUMBER_LASTIdentifies last number in range.NUMBER_LAST_SUFFIXSuffix for the last number in range.LOT_NUMBER_PREFIXLot number prefix.LOT_NUMBERLot number.LOT_NUMBER_SUFFIXLot number suffix.STREET_NAMEStreet name. e.g. "POPLAR".STREET_TYPEStreet type in full text (e.g. AVENUE, PARADE, STREET).STREET_SUFFIXStreet suffix in full text (e.g. EAST, WEST).LOCALITY_NAMEThe name of the locality or suburb.STATEThe state or territory abbreviation.POSTCODEPostcode.LEGAL_PARCEL_IDGeneric parcel id field derived from the Geoscape Australia’s Cadastre parcel where available.MB_CODEThe mesh block code.MB_CENSUS_YEARThe ABS Census Year the mesh block codes is extracted from.GEOCODE_TYPEName of the geocode type.LEVEL_GEOCODEDName of the geocode level type code.ADDRESS_TYPEAddress type (e.g. "Postal", Physical").PRIVATE_STREETPrivate street information.ALIAS_PRINCIPALA = Alias record, P = Principal record.ALIAS_TYPEAlias type (e.g. "Synonym").PRINCIPAL_PIDPersistent identifier (i.e. ADDRESS_DETAIL_PID) of the principal address.PRIMARY_SECONDARYIndicator that identifies if the address is P (Primary) or S (Secondary).PRIMARY_PIDPersistent identifier for the primary address.LONGITUDELongitude.LATITUDELatitude.CONFIDENCEReflects how many contributor databases this address appears in (0 = 1 database, 1 = 2 database etc.).ContactThe Digital Atlas of Australia: digitalatlas@ga.gov.au