cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4a3u3p
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia cuernavacana",
 "Year 1987-2026"
 ]
}

 The dataset includes 62 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001578-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.85tjp7
title: Occurrence Download

description: A dataset containing 10025 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Compilación de datos de campo provenientes del levantamiento del Inventario Forestal Nacional (2015-2017), datos sin validar"
 ]
}

 The dataset includes 10025 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001579-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6w6hnf
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia dieringeri",
 "Year 1979-2026"
 ]
}

 The dataset includes 30 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001582-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18407967
title: Processed quasi-3D seismic dataset and extracted horizons, R/V Tangaroa research voyage TAN2006 (2020)

description: TAN2006_cube.sgy: Processed quasi-3D seismic dataset.





Byte order: Big Endian




Trace sample format: IEEE (floating point)




SEG-Y text header (ASCII format):



C01 Voyage TAN2006:     Geologic Champagne - Chatham Rise
C02                     26 June - 17 July 2020
C03
C04 Voyage Lead:        Ingo Pecher (University of Auckland)
C05 Vessel:             R/V Tangaroa
C06 Processing:         Fynn Warnke (University of Auckland)
C07
C08 Source:             GI-Gun 45/105 cu inch
C09 Receiver:           Geometrics 47/48 Channel Streamer (NIWA)
C10 Group interval:     12.5 m
C11
C12 Map projection:     WGS84 / UTM 60 south (EPSG:32760)
C13 Coordinate units:   Centimeter (scale: -100)
C14 Bytes location:     181:184 (CDP_X), 185:188 (CDP_Y)
C15
C16 3D GEOMETRY (right-handed)
C17 Grid origin:        296983 E / 5121801 S
C18 Inline azimuth:     348
C19 Crossline azimuth:  78
C20 Inline spacing:     6.25 m
C21 Crossline spacing:  6.25 m
C22 Inline multiplier:  10000
C23 Inline ID:          1000:1332/1  (n= 333)
C24 Crossline ID:       1000:2004/1  (n=1005)
C25
C26 PROCESSING
C27 Pre-stack:          BULKSHIFT (15 ms). BANDPASS (10/20-300/350). BALANCE
C28                     DESPIKEH. FKMUTE. SPHDIV (water velocity). SMUTE (top)
C29                     PREDICTIVE DECONVOLUTION
C30                     SIGNATURE DECONVOLUTION (zero-phase). RESAMPLE (2 ms)
C31                     DEMULTIPLE (water velocity)
C32                     RMS VELOCITY (RAPTOR). STATIC CORRECTION (CDP-aligned)
C33                     5D INTERPOLATION (STITCH)
C34                     RMS VELOCITY (RAPTOR). STATIC CORRECTION (CDP-aligned)
C35                     3D KIRCHHOFF PRE-STACK TIME MIGRATION
C36                     SPHDIV (RMS)
C37 Post-stack:         STACK
C38                     INVERSE Q. FDFILT (10/20-200/240). SMUTE (top)
C39 SEG Y REV1
C40 END ASCII

Seismic_horizons_3D.dat: Mapped seismic horizons (ASCII format)





Columns: Name, X, Y, Inline, Crossline, TWT_ms




Coordinate reference system: WGS84 / UTM 60 south (EPSG:32760)
DOI: 10.5281/zenodo.18407968
title: Processed quasi-3D seismic dataset and extracted horizons, R/V Tangaroa research voyage TAN2006 (2020)

description: TAN2006_cube.sgy: Processed quasi-3D seismic dataset.





Byte order: Big Endian




Trace sample format: IEEE (floating point)




SEG-Y text header (ASCII format):



C01 Voyage TAN2006:     Geologic Champagne - Chatham Rise
C02                     26 June - 17 July 2020
C03
C04 Voyage Lead:        Ingo Pecher (University of Auckland)
C05 Vessel:             R/V Tangaroa
C06 Processing:         Fynn Warnke (University of Auckland)
C07
C08 Source:             GI-Gun 45/105 cu inch
C09 Receiver:           Geometrics 47/48 Channel Streamer (NIWA)
C10 Group interval:     12.5 m
C11
C12 Map projection:     WGS84 / UTM 60 south (EPSG:32760)
C13 Coordinate units:   Centimeter (scale: -100)
C14 Bytes location:     181:184 (CDP_X), 185:188 (CDP_Y)
C15
C16 3D GEOMETRY (right-handed)
C17 Grid origin:        296983 E / 5121801 S
C18 Inline azimuth:     348
C19 Crossline azimuth:  78
C20 Inline spacing:     6.25 m
C21 Crossline spacing:  6.25 m
C22 Inline multiplier:  10000
C23 Inline ID:          1000:1332/1  (n= 333)
C24 Crossline ID:       1000:2004/1  (n=1005)
C25
C26 PROCESSING
C27 Pre-stack:          BULKSHIFT (15 ms). BANDPASS (10/20-300/350). BALANCE
C28                     DESPIKEH. FKMUTE. SPHDIV (water velocity). SMUTE (top)
C29                     PREDICTIVE DECONVOLUTION
C30                     SIGNATURE DECONVOLUTION (zero-phase). RESAMPLE (2 ms)
C31                     DEMULTIPLE (water velocity)
C32                     RMS VELOCITY (RAPTOR). STATIC CORRECTION (CDP-aligned)
C33                     5D INTERPOLATION (STITCH)
C34                     RMS VELOCITY (RAPTOR). STATIC CORRECTION (CDP-aligned)
C35                     3D KIRCHHOFF PRE-STACK TIME MIGRATION
C36                     SPHDIV (RMS)
C37 Post-stack:         STACK
C38                     INVERSE Q. FDFILT (10/20-200/240). SMUTE (top)
C39 SEG Y REV1
C40 END ASCII

Seismic_horizons_3D.dat: Mapped seismic horizons (ASCII format)





Columns: Name, X, Y, Inline, Crossline, TWT_ms




Coordinate reference system: WGS84 / UTM 60 south (EPSG:32760)
DOI: 10.15468/dl.x8u6y2
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Naja savannula"
 ]
}

 The dataset includes 43 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001584-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3bywwp
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia discolor",
 "Year 1976-2026"
 ]
}

 The dataset includes 17 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001583-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ekz825
title: Occurrence Download

description: A dataset containing 718 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 28.773895969281842
 AND decimallatitude &lt;= 33.06103686093294
 AND decimallongitude &gt;= 86.60801420953611
 AND decimallongitude &lt;= 94.62398123724668
 AND GBIF_Within('POLYGON ((94.623981 28.773896, 94.623981 33.061037, 86.608014 33.061037, 86.608014 28.773896, 94.623981 28.773896))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 718 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001580-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x2qsum
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia dissiticeps"
 ]
}

 The dataset includes 11 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001585-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dewh6q
title: Occurrence Download

description: A dataset containing 633243 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is eButterfly Surveys"
 ]
}

 The dataset includes 633243 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001581-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxynnm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((156.09851 -63.99054,190.4435 -63.99054,190.4435 -23.06066,156.09851 -23.06066,156.09851 -63.99054))",
 "TaxonKey is Micarea prasinastra"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001587-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/xtaqmc
title: Growth Projections and Complexity Rankings

description: &lt;p&gt;
Each year, researchers at Harvard's Growth Lab release growth forecasts for the upcoming decade as well as annual rankings of countries by economic complexity. The Economic Complexity Index (ECI) ranking is a measure of the amount of capabilities and knowhow of a given country determined by the diversity, ubiquity, and complexity of the products it exports.
&lt;p&gt;

&lt;p&gt;
Growth projections are calculated through a process largely based on determining whether a country's economic complexity is higher or lower than expected given its level of income. We expect countries whose economic complexity is greater than we would expect for its level of income to grow faster than those that are "too rich" for their current level of complexity. In this data, a country's growth projection value for a given year is for the decade beginning with that year. For example, a value in a 2017 row is the projection of annualized growth for 2017–2027.
&lt;p&gt;
DOI: 10.7910/dvn/h8sfd2
title: International Trade Data (SITC, Rev. 2)

description: &lt;p&gt;
&lt;a href="http://atlas.hks.harvard.edu"&gt;The Atlas of Economic Complexity&lt;/a&gt; maintains trade data in multiple international classification systems. This data set contains trade flows classified via Standard International Trade Classification (SITC) Revision 2. In comparison to Harmonized System (HS), SITC data offers a longer time-series but covers fewer goods, as the goods classification names had to remain rather consistent since the 1960s, despite the onset of new goods segments or products since (e.g. electronics):
&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;Categorizes approximately 700 goods&lt;/li&gt;
 &lt;li&gt;Data coverage starts in 1962&lt;/li&gt;
 &lt;li&gt;Categories break down to 1-, 2-, or 4-digit detail levels&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;
Raw data on trade in goods is provided by &lt;a href="https://comtrade.un.org/"&gt;United Nations Statistical Division (COMTRADE)&lt;/a&gt;. The data is then cleaned by Growth Lab researchers using the Bustos-Yildirim Method which uses bilateral trade flows to account for inconsistent reporting and provides more reliable accounting.
&lt;/p&gt;

&lt;p&gt;
A data dictionary for this repository describing columns within these files can be found under "sitc_data_dictionary.csv".
&lt;/p&gt;
DOI: 10.7910/dvn/yavjdf
title: International Trade Data (HS, 12)

description: &lt;p&gt;
&lt;a href="http://atlas.hks.harvard.edu"&gt;The Atlas of Economic Complexity&lt;/a&gt; maintains trade data in multiple international classification systems. This data set contains trade flows classified via Harmonized System (HS) 2012. HS data offers a contemporary and detailed classification of goods, but covers a relatively short time period: 
&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;Categorizes approximately 5,200 goods&lt;/li&gt;
 &lt;li&gt;Coverage starts in year 2012&lt;/li&gt;
 &lt;li&gt;Categories break down to 1-, 2-, 4-, or 6-digit detail levels (though country reporting can be less reliable at the 6-digit level)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;
Raw data on trade in goods is provided by &lt;a href="https://comtrade.un.org/"&gt;United Nations Statistical Division (COMTRADE)&lt;/a&gt;. The data is then cleaned by Growth Lab researchers using the Bustos-Yildirim Method which uses bilateral trade flows to account for inconsistent reporting and provides more reliable accounting.
&lt;/p&gt;

&lt;p&gt;
A data dictionary for this repository describing columns within these files can be found under "hs12_data_dictionary.csv".
&lt;/p&gt;
DOI: 10.15468/dl.k76ggc
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia dugesii",
 "Year 1984-2026"
 ]
}

 The dataset includes 135 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001588-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15678346
title: Panicle Hydrangea data on flower visiting insects

description: Partial Abstract: As available wild habitat for pollinators shrinks, urban and suburban plantings have become increasingly important for maintaining pollinators on the landscape. Hydrangeas are some of the most popular ornamental shrubs in the United States, and panicle hydrangeas in particular have seen a recent surge in popularity. The University of Wisconsin – Madison Arboretum has one of the largest panicle hydrangea collections in the country. We assessed flower-visiting insect assemblages across 46 cultivars of panicle hydrangeas (H. paniculata) and 16 cultivars of smooth hydrangeas (H. arborescens). We collected three years of photo (N = 13,017), visual (N = 776), and physical (N = 1,156) samples of insects. 

This data is currently being submitted for publication. We will open records to the public after it is published. 

This dataset includes: Excel files of data, photo vouchers of insects. 

Funding was made possible due to the Stanley Smith Horticultural Trust.
DOI: 10.5281/zenodo.15677865
title: Panicle Hydrangea data on flower visiting insects

description: Partial Abstract: As available wild habitat for pollinators shrinks, urban and suburban plantings have become increasingly important for maintaining pollinators on the landscape. Hydrangeas are some of the most popular ornamental shrubs in the United States, and panicle hydrangeas in particular have seen a recent surge in popularity. The University of Wisconsin – Madison Arboretum has one of the largest panicle hydrangea collections in the country. We assessed flower-visiting insect assemblages across 46 cultivars of panicle hydrangeas (H. paniculata) and 16 cultivars of smooth hydrangeas (H. arborescens). We collected three years of photo (N = 13,017), visual (N = 776), and physical (N = 1,156) samples of insects. 

This data is currently being submitted for publication. We will open records to the public after it is published. 

This dataset includes: Excel files of data, photo vouchers of insects. 

Funding was made possible due to the Stanley Smith Horticultural Trust.
DOI: 10.15468/dl.ufkvhj
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Naja savannula"
 ]
}

 The dataset includes 30 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001590-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18525169
title: Neutrino thermalization via randomization on a quantum processor

description: Dataset generated in the context of the following paper: https://arxiv.org/abs/2510.24841.

This dataset contains experimental and processed data from quantum simulations of all-to-all spin Hamiltonians with random couplings, modeling neutrino flavor evolution in supernovae. The data includes raw measurement counts from IBM quantum devices, readout calibration matrices, twirling data for error mitigation, and computed expectation values with different levels of error correction and symmetry verification. 

These simulations were performed using random quantum circuits to emulate non-local dynamics in systems of up to over 100 qubits. The dataset enables analysis of thermalization behavior, providing access to both raw hardware outputs and processed observables for reproducibility and further study. 

The dataset is organized by the number of qubits and by Hamiltonian seed. Each seed folder contains the following data types:





Raw counts (counts_*.npy): Direct measurement results from IBM quantum devices.




Calibration matrices (cals_*.npy): Single-qubit readout calibrations for each time step.




Twirling data (trex_.npy, trex_nr_.npy): Used to mitigate readout errors and normalize measurement results.




Observables (observable_*.npy): Computed expectation values of qubit measurements. Variants correspond to different error-mitigation methods:





Raw




Noise renormalized (_nr)




Symmetry-verified (_sv)




Both renormalized and symmetry-verified (_nr_sv)





The repository also includes scripts for data generation and processing:





hardware_run.py: Prepares quantum circuits, submits jobs to IBM quantum devices, and stores raw counts.




post_processing.py: Loads the raw data and applies the error mitigation pipeline.




requirements.txt: Lists Python dependencies for reproducing the data processing environment.
DOI: 10.5281/zenodo.18525168
title: Neutrino thermalization via randomization on a quantum processor

description: Dataset generated in the context of the following paper: https://arxiv.org/abs/2510.24841.

This dataset contains experimental and processed data from quantum simulations of all-to-all spin Hamiltonians with random couplings, modeling neutrino flavor evolution in supernovae. The data includes raw measurement counts from IBM quantum devices, readout calibration matrices, twirling data for error mitigation, and computed expectation values with different levels of error correction and symmetry verification. 

These simulations were performed using random quantum circuits to emulate non-local dynamics in systems of up to over 100 qubits. The dataset enables analysis of thermalization behavior, providing access to both raw hardware outputs and processed observables for reproducibility and further study. 

The dataset is organized by the number of qubits and by Hamiltonian seed. Each seed folder contains the following data types:





Raw counts (counts_*.npy): Direct measurement results from IBM quantum devices.




Calibration matrices (cals_*.npy): Single-qubit readout calibrations for each time step.




Twirling data (trex_.npy, trex_nr_.npy): Used to mitigate readout errors and normalize measurement results.




Observables (observable_*.npy): Computed expectation values of qubit measurements. Variants correspond to different error-mitigation methods:





Raw




Noise renormalized (_nr)




Symmetry-verified (_sv)




Both renormalized and symmetry-verified (_nr_sv)





The repository also includes scripts for data generation and processing:





hardware_run.py: Prepares quantum circuits, submits jobs to IBM quantum devices, and stores raw counts.




post_processing.py: Loads the raw data and applies the error mitigation pipeline.




requirements.txt: Lists Python dependencies for reproducing the data processing environment.
DOI: 10.15468/dl.96g58j
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "InstitutionKey" : [
 "is 1930ea05-2c10-4c07-bdcf-af8bbf1d3a9b"
 ]
}

 The dataset includes 46 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001591-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/3bal1o
title: Classifications Data

description: &lt;p&gt;
This repository contains metadata on the international classification systems for locations and products presented in &lt;a href="http://atlas.hks.harvard.edu"&gt;The Atlas of Economic Complexity&lt;/a&gt;. For products, these include HS (1992 and 2012 versions), SITC, and Unilateral Services. Note that these classifications are slightly augmented for use in the Atlas and do not exactly match their base systems.
&lt;/p&gt;
&lt;p&gt;
A data dictionary for this repository describing columns within these files can be found under "classification_data_dictionary.csv".
&lt;/p&gt;
DOI: 10.7910/dvn/fcdzbn
title: Product Space Networks

description: &lt;p&gt;
The Product Space layout is provided in CSV format. The node data (umap_layout_hs92.csv) contains product codes, (X, Y) coordinates, and cluster names. The coordinates represent UMAP embeddings of a proximity matrix, where distances between products are meaningful and represent their relatedness based on whether products are co-exported by countries. Products have also been clustered into knowledge clusters based on co-export patterns. 
&lt;/p&gt;
&lt;p&gt;
The edge data (top_edges_hs92.csv) is an adjacency list with source and target fields matching the product codes, used to show top relatedness links between products for visualization purposes. Note that the node positions in the node data consider the entire proximity matrix, while the edge data only shows the top relatedness links for visualization purposes. The layout has been optimized for visualization, with uninformative links removed.
&lt;/p&gt;
&lt;p&gt;
A data dictionary for this repository describing columns within these files can be found under "product_space_data_dictionary.csv".
&lt;/p&gt;
DOI: 10.15468/dl.vms74f
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia durangensis"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001592-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.