cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14873693
title: Geospatial Electric Power Grid Datasets for Mazoon Electricity Company in Oman

description: This repository offers the geospatial power infrastructure dataset for the Mazoon power Company (MZEC) network in Oman. The dataset encompasses the geographic characteristics of several network components, including electrical poles, service points, and substations, throughout four governorates and seventeen districts in Oman.  

Contents:- Poles_MZEC.csv (507,251 records) – Locations of electricity distribution poles.- ServicePoints_MZEC.csv (385,050 records) – Locations of electricity service points.- Substations_MZEC.csv (23,623 records) – Locations of substations.

Data Fields: Each file contains:- GOVERNORAT – Governorate where the infrastructure is located.- DISTRICT – Specific district within the governorate.- UTM_Easting –  Universal Transverse Mercator Easting coordinate- UTM_Northing – Universal Transverse Mercator Northing coordinate

The following table summarizes the geographical coverage of the dataset:

| Region/Governorate    | Districts Covered ||--------------------------|----------------------|| Ad Dakhliyah                | Adam, Bahla, Izki, Nizwa, Sumail || North Al Sharqiyah       | Al Mudaibi, Bidyah, Dima Tayeen, Ibra || South Al Batinah           | Barka, Musnaah, Nakhl, Rustaq, Suwaiq || South Al Sharqiyah       | JBBA, JBBH, Sur |

---

Dataset 2: Nigerian Electricity Transmission Database (ETDB)As a supplementary dataset, this repository also includes spatial data on 56,283 transmission grid components in Nigeria [Sourced from: The Humanitarian Data Exchange, from https://data.humdata.org/dataset/ electricaldistributiongridmaps].

Contents:-Nigeria_ETDB.csv (56,283 records) – Locations of high-voltage transmission grid components.

Data Fields:- lat – Latitude coordinate.- lon – Longitude coordinate.- Region – Region of Nigeria where the component is located.- Zone – Transmission grid zone.

Regional and Local Levels of Nigerian Transmission Grid  The dataset covers multiple regions and zones in Nigeria, structured as follows:

| Region  | Zones Covered ||------------|------------------|| Region 1| Zone 111, 112, 121, 122 || Region 2 | Zone 211, 221, 222 || Region 3 | Zone 311, 312, 322, 332 || Region 4 | Zone 411, 412, 421, 422, 423 |

To ensure the security of sensitive location data for the Omani dataset, all latitude, longitude, UTM easting, and UTM northing values have been scaled between 0 and 1. However, the scaling preserves the relative spatial relationships necessary for accurate geospatial modeling without exposing exact geographic coordinates.
DOI: 10.5281/zenodo.14873694
title: Geospatial Electric Power Grid Datasets for Mazoon Electricity Company in Oman

description: This repository offers the geospatial power infrastructure dataset for the Mazoon power Company (MZEC) network in Oman. The dataset encompasses the geographic characteristics of several network components, including electrical poles, service points, and substations, throughout four governorates and seventeen districts in Oman.  

Contents:- Poles_MZEC.csv (507,251 records) – Locations of electricity distribution poles.- ServicePoints_MZEC.csv (385,050 records) – Locations of electricity service points.- Substations_MZEC.csv (23,623 records) – Locations of substations.

Data Fields: Each file contains:- GOVERNORAT – Governorate where the infrastructure is located.- DISTRICT – Specific district within the governorate.- UTM_Easting –  Universal Transverse Mercator Easting coordinate- UTM_Northing – Universal Transverse Mercator Northing coordinate

The following table summarizes the geographical coverage of the dataset:

| Region/Governorate    | Districts Covered ||--------------------------|----------------------|| Ad Dakhliyah                | Adam, Bahla, Izki, Nizwa, Sumail || North Al Sharqiyah       | Al Mudaibi, Bidyah, Dima Tayeen, Ibra || South Al Batinah           | Barka, Musnaah, Nakhl, Rustaq, Suwaiq || South Al Sharqiyah       | JBBA, JBBH, Sur |

---

Dataset 2: Nigerian Electricity Transmission Database (ETDB)As a supplementary dataset, this repository also includes spatial data on 56,283 transmission grid components in Nigeria [Sourced from: The Humanitarian Data Exchange, from https://data.humdata.org/dataset/ electricaldistributiongridmaps].

Contents:-Nigeria_ETDB.csv (56,283 records) – Locations of high-voltage transmission grid components.

Data Fields:- lat – Latitude coordinate.- lon – Longitude coordinate.- Region – Region of Nigeria where the component is located.- Zone – Transmission grid zone.

Regional and Local Levels of Nigerian Transmission Grid  The dataset covers multiple regions and zones in Nigeria, structured as follows:

| Region  | Zones Covered ||------------|------------------|| Region 1| Zone 111, 112, 121, 122 || Region 2 | Zone 211, 221, 222 || Region 3 | Zone 311, 312, 322, 332 || Region 4 | Zone 411, 412, 421, 422, 423 |

To ensure the security of sensitive location data for the Omani dataset, all latitude, longitude, UTM easting, and UTM northing values have been scaled between 0 and 1. However, the scaling preserves the relative spatial relationships necessary for accurate geospatial modeling without exposing exact geographic coordinates.
DOI: 10.5281/zenodo.7117784
title: Datasets for napari-lattice: Lattice Lightsheet Analysis

description: Sample data acquired using the Zeiss lattice lightsheet microscope. The images are of human red blood cells acquired in the 488 channel. This dataset is associated with the software napari-lattice, which is a plugin for napari, an n-dimensional viewer in Python. Dimensions are in the format (Time, Channel, Z, Y, X) RBC_full_one_timepoint.czi: (1, 1, 834, 300, 2048) RBC_full_time_series.czi: (3, 1, 834, 300, 2048) RBC_medium_LLSZ.czi: (5, 1, 834, 297, 279) RBC_tiny.czi: (1, 1, 834, 118, 209) Voxel sizes in z, y and x respectively: ( 0.3, 0.1449922, 0.1449922) napari-lattice can process lattice lightsheet datasets skewed in the X or Y direction, and you can also create full analysis workflows in napari. For more information: https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
description: {"references": ["Geoghegan, N.D., Evelyn, C., Whitehead, L.W. et al. 4D analysis of malaria parasite invasion offers insights into erythrocyte membrane remodeling and parasitophorous vacuole formation. Nat Commun 12, 3620 (2021). https://doi.org/10.1038/s41467-021-23626-7"]}
DOI: 10.5281/zenodo.14903188
title: Datasets for napari-lattice: Lattice Lightsheet Analysis

description: Sample data acquired using the Zeiss lattice lightsheet microscope. The images are of human red blood cells acquired in the 488 channel. This dataset is associated with the software napari-lattice, which is a plugin for napari, an n-dimensional viewer in Python.

Dimensions are in the format (Time, Channel, Z, Y, X)



RBC_full_one_timepoint.czi: (1, 1,  834, 300, 2048)

RBC_full_time_series.czi: (3, 1,  834, 300, 2048)

RBC_medium_LLSZ.czi: (5, 1,  834, 297, 279)

RBC_tiny.czi:  (1, 1,  834, 118, 209)


Voxel sizes in z, y and x respectively: ( 0.3, 0.1449922, 0.1449922)

We have updated the dataset with pointspread functions (PSFs) for each channel. These are Zeiss simulated ones. There is a czi version and a tiff version. 

If you would like more PSFs, visit this comprehensive repo from EPFL: https://zenodo.org/records/14505724

 

napari-lattice can process lattice lightsheet datasets from Zeiss Lattice lightsheet 7. We have updated the interface and API. Visit:

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.5281/zenodo.7117783
title: Datasets for napari-lattice: Lattice Lightsheet Analysis

description: Sample data acquired using the Zeiss lattice lightsheet microscope. The images are of human red blood cells acquired in the 488 channel. This dataset is associated with the software napari-lattice, which is a plugin for napari, an n-dimensional viewer in Python.

Dimensions are in the format (Time, Channel, Z, Y, X)



RBC_full_one_timepoint.czi: (1, 1,  834, 300, 2048)

RBC_full_time_series.czi: (3, 1,  834, 300, 2048)

RBC_medium_LLSZ.czi: (5, 1,  834, 297, 279)

RBC_tiny.czi:  (1, 1,  834, 118, 209)


Voxel sizes in z, y and x respectively: ( 0.3, 0.1449922, 0.1449922)

We have updated the dataset with pointspread functions (PSFs) for each channel. These are Zeiss simulated ones. There is a czi version and a tiff version. 

If you would like more PSFs, visit this comprehensive repo from EPFL: https://zenodo.org/records/14505724

 

napari-lattice can process lattice lightsheet datasets from Zeiss Lattice lightsheet 7. We have updated the interface and API. Visit:

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.15468/dl.j99p55
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Estudios bióticos: Plantas, edafofauna epígea, anfibios y aves de los Complejos de Páramos de Sonsón y Urrao",
 "TaxonKey is Quercus humboldtii Bonpl."
 ]
}

 The dataset includes 51 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004328-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6075/j0w09646
title: Data from: Microfluidic platform for coupled studies of freezing behavior and final effloresced particle morphology in Snomax® containing aqueous droplets. In Center for Aerosol Impacts on Chemistry of the Environment (CAICE)
DOI: 10.6084/m9.figshare.28355897
title: <b>MH-1M Dataset</b>

description: The rapid and widespread increase of Android malware presents substantial obstacles to cybersecurity research. In order to revolutionize the field of malware research, we present the MH-1M dataset, which is a thorough compilation of <b>1,340,515 APK</b> samples. This dataset encompasses a wide range of diverse attributes and metadata, offering a comprehensive perspective. The utilization of the VirusTotal API guarantees precise assessment of threats by amalgamating various detection techniques. Our research indicates that MH-1M is a highly current dataset that provides valuable insights into the changing nature of malware.MH-1M consists of 23,247 features that cover a wide range of application behavior, from intents::accept to apicalls::landroid/window/splashscreenview.remove. The features are categorized into four primary classifications:Feature TypesValuesAPICalls22,394Intents407OPCodes232Permissions214The dataset is stored efficiently, utilizing a memory capacity of <b>29.0 GB</b>, which showcases its substantial yet controllable magnitude. The dataset consists of <b>1,221,421 benign</b> applications and <b>119,094 malware</b> applications, ensuring a balanced representation for accurate malware detection and analysis.The MH-1M repository also offers a wide variety of metadata from APKs, providing useful data into the development of malicious software over a period of more than ten years. The Android features include a wide variety of metadata, which includes SHA256 hashes, file names, package names, compilation APIs, and various other details. This GitHub repository contains over 400GB of valuable data, making it the largest and most comprehensive dataset available for advancing research and development in Android malware detection.
DOI: 10.6084/m9.figshare.28355897.v1
title: <b>MH-1M Dataset</b>

description: The rapid and widespread increase of Android malware presents substantial obstacles to cybersecurity research. In order to revolutionize the field of malware research, we present the MH-1M dataset, which is a thorough compilation of <b>1,340,515 APK</b> samples. This dataset encompasses a wide range of diverse attributes and metadata, offering a comprehensive perspective. The utilization of the VirusTotal API guarantees precise assessment of threats by amalgamating various detection techniques. Our research indicates that MH-1M is a highly current dataset that provides valuable insights into the changing nature of malware.MH-1M consists of 23,247 features that cover a wide range of application behavior, from intents::accept to apicalls::landroid/window/splashscreenview.remove. The features are categorized into four primary classifications:Feature TypesValuesAPICalls22,394Intents407OPCodes232Permissions214The dataset is stored efficiently, utilizing a memory capacity of <b>29.0 GB</b>, which showcases its substantial yet controllable magnitude. The dataset consists of <b>1,221,421 benign</b> applications and <b>119,094 malware</b> applications, ensuring a balanced representation for accurate malware detection and analysis.The MH-1M repository also offers a wide variety of metadata from APKs, providing useful data into the development of malicious software over a period of more than ten years. The Android features include a wide variety of metadata, which includes SHA256 hashes, file names, package names, compilation APIs, and various other details. This GitHub repository contains over 400GB of valuable data, making it the largest and most comprehensive dataset available for advancing research and development in Android malware detection.
DOI: 10.15468/dl.3u52kg
title: Occurrence Download

description: A dataset containing 1824496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Geometry POLYGON((-114.54 31.01,-97.6 31.01,-97.6 37.38,-114.54 37.38,-114.54 31.01))",
 "HasCoordinate is true",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 1824496 records from 424 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004317-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14901918
title: Wotton and White: Supplemental Material: Functional retinal physiology

description: The respository contains raw data, Rdata files from the analyses and an index file to identify gene names. There is a read me document included which describes the folder contents.
DOI: 10.15468/dl.rjdcue
title: Occurrence Download

description: A dataset containing 501528889 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tracheophyta"
 ]
}

 The dataset includes 501528889 records from 15265 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004321-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zyceda
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynopotamus kincaidi (Schultz, 1950)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 86 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076295-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ya6vh
title: Occurrence Download

description: A dataset containing 18989228 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "DatasetKey is one of (EOD – eBird Observation Dataset)"
 ]
}

 The dataset includes 18989228 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076267-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cn5rjr
title: Occurrence Download

description: A dataset containing 540150 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Tercer Inventario Forestal Nacional. Ministerio de Agricultura, Alimentación y Medio Ambiente (España/Spain)"
 ]
}

 The dataset includes 540150 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076287-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atsdrh
title: Occurrence Download

description: A dataset containing 671 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-124.66868 44.50445,-123.4841 44.19643,-121.24556 48.84944,-123.42414 50.40594,-127.40202 53.676,-128.19586 54.65697,-129.16948 55.47968,-129.95657 56.22302,-131.25522 54.97629,-132.75285 54.60301,-134.07732 53.97114,-131.81379 50.62424,-124.66868 44.50445))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salicornia pacifica Standl."
 ]
}

 The dataset includes 671 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076297-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.23fadq
title: Occurrence Download

description: A dataset containing 1725 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stenotritidae"
 ]
}

 The dataset includes 1725 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076298-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.824a9d
title: Occurrence Download

description: A dataset containing 515514 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate out of range, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate accuracy invalid, Coordinate uncertainty meters invalid, Coordinate precision/uncertainty mismatch, Footprint SRS invalid, Footprint WKT mismatch, Footprint WKT invalid, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date mismatch, Recorded date invalid, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names , Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count, ccurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantago major L.",
 "Year 2000-2024"
 ]
}

 The dataset includes 515514 records from 2608 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076288-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0210326.10fu
title: SH0210326.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0210327.10fu
title: SH0210327.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0210328.10fu
title: SH0210328.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.