cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zcyd5wvncc
title: Monthly Sargassum Wet Biomass Estimates in the Western North Atlantic from MODIS Satellite Observations

description: This dataset provides monthly mean Sargassum wet biomass estimates (in million metric tons) from March 2000 to February 2024, derived from MODIS observations using the methodology described in Hu et al. (2023). The data cover several subregions, including the North Sargasso Sea (NSS), South Sargasso Sea (SSS), Gulf Stream Region (GSR), Antilles Current Region (ACR), Great Atlantic Sargassum Belt (GASB), and the Northwestern Gulf of Mexico (NW_GoM). The locations of these subregions are provided in the attached READ ME file.

Briefly, each image pixel is classified into one of the three types using a deep-learning computer model: Sargassum containing, Sargassum free, and invalid observation (clouds, cloud shadows, strong sun glint, etc). Then the Sargassum containing pixels are further spectrally unmixed to determine the subpixel percent (%) cover within each pixel. For a pre-gridded Sargassum map, the mean Sargassum percent cover in each 0.5-degree grid is calculated as the arithmetic average of all image pixels (both Sargassum containing and Sargassum free) in that grid collected by the satellite during that calendar month. Such a mean percent cover is converted to Sargassum wet biomass using a calibration constant of 3.34 kg per square meter of Sargassum determined from field measurements (Wang et al., 2018). Integration of the grid-specific biomass across all grids within a subregion leads to the mean Sargassum biomass for that subregion. These steps were applied to MODIS/Terra (2000 – 2024) and MODIS/Aqua (2002 – 2024) separately, with the final maps being the arithmetic average between the two. 

In the above steps, all MODIS data were downloaded from NASA OB.DAAC (https://oceancolor.gsfc.nasa.gov) and processed using the NASA software SeaDAS (version 8.0). The deep-learning model and spectral unmixing model as well as the method to calculate monthly means were all developed at the Optical Oceanography Lab of the University of South Florida using computer codes developed in house. The daily and weekly Sargassum maps have been made available through their Sargassum Watch System (SaWS) website: https://optics.marine.usf.edu/projects/saws.html.

Hu, C., Zhang, S., Barnes, B.B., Xie, Y., Wang, M., Cannizzaro, J.P., & English, D.C. (2023). Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi‐band medium‐resolution satellite data and deep learning. Remote Sensing of Environment, 289, 113515. https://doi.org/10.1016/j.rse.2023.113515. 

Wang, M., Hu, C., Cannizzaro, J., English, D., Han, X., Naar, D., Lapointe, B., Brewton, R. and Hernandez, F. (2018). Remote sensing of Sargassum biomass, nutrients, and pigments. Geophysical Research Letters, 45(22), pp.12-359. https://doi.org/10.1029/2018GL078858.
DOI: 10.17632/zcyd5wvncc.1
title: Monthly Sargassum Wet Biomass Estimates in the Western North Atlantic from MODIS Satellite Observations

description: This dataset provides monthly mean Sargassum wet biomass estimates (in million metric tons) from March 2000 to February 2024, derived from MODIS observations using the methodology described in Hu et al. (2023). The data cover several subregions, including the North Sargasso Sea (NSS), South Sargasso Sea (SSS), Gulf Stream Region (GSR), Antilles Current Region (ACR), Great Atlantic Sargassum Belt (GASB), and the Northwestern Gulf of Mexico (NW_GoM). The locations of these subregions are provided in the attached READ ME file.

Briefly, each image pixel is classified into one of the three types using a deep-learning computer model: Sargassum containing, Sargassum free, and invalid observation (clouds, cloud shadows, strong sun glint, etc). Then the Sargassum containing pixels are further spectrally unmixed to determine the subpixel percent (%) cover within each pixel. For a pre-gridded Sargassum map, the mean Sargassum percent cover in each 0.5-degree grid is calculated as the arithmetic average of all image pixels (both Sargassum containing and Sargassum free) in that grid collected by the satellite during that calendar month. Such a mean percent cover is converted to Sargassum wet biomass using a calibration constant of 3.34 kg per square meter of Sargassum determined from field measurements (Wang et al., 2018). Integration of the grid-specific biomass across all grids within a subregion leads to the mean Sargassum biomass for that subregion. These steps were applied to MODIS/Terra (2000 – 2024) and MODIS/Aqua (2002 – 2024) separately, with the final maps being the arithmetic average between the two. 

In the above steps, all MODIS data were downloaded from NASA OB.DAAC (https://oceancolor.gsfc.nasa.gov) and processed using the NASA software SeaDAS (version 8.0). The deep-learning model and spectral unmixing model as well as the method to calculate monthly means were all developed at the Optical Oceanography Lab of the University of South Florida using computer codes developed in house. The daily and weekly Sargassum maps have been made available through their Sargassum Watch System (SaWS) website: https://optics.marine.usf.edu/projects/saws.html.

Hu, C., Zhang, S., Barnes, B.B., Xie, Y., Wang, M., Cannizzaro, J.P., & English, D.C. (2023). Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi‐band medium‐resolution satellite data and deep learning. Remote Sensing of Environment, 289, 113515. https://doi.org/10.1016/j.rse.2023.113515. 

Wang, M., Hu, C., Cannizzaro, J., English, D., Han, X., Naar, D., Lapointe, B., Brewton, R. and Hernandez, F. (2018). Remote sensing of Sargassum biomass, nutrients, and pigments. Geophysical Research Letters, 45(22), pp.12-359. https://doi.org/10.1029/2018GL078858.
DOI: 10.15468/dl.s7c4jt
title: Occurrence Download

description: A dataset containing 182824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Anas platyrhynchos)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 182824 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015135-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11588/data/lspj6b
title: Legislative bargaining with private information: Experimental evidence comparing unanimity and majority rule [Dataset]

description: This paper experimentally investigates behavior in a legislative bargaining game with private information. A single proposer is seeking to pass a proposal. In each of two rounds, she attempts to buy the necessary votes by offering payments. Between rounds, the game ends in breakdown with a certain probability. The two responders hold privately known disagreement values. We compare behavior under majority (one vote needed) vs. unanimity (both votes needed) rule. The main theoretical prediction is that responders are more ``expensive'' in round 1 under unanimity rule because there exists a signaling incentive to vote ``no''. Under majority rule, this incentive is absent and in fact, responders should fear being excluded after voting ``no''. Our experimental findings confirm the presence of signaling incentives under unanimity rule, resulting in lower agreement probabilities than under majority rule. In contrast, the experimental evidence under majority rule is mixed and does not fully coincide with the theoretical predictions.
DOI: 10.15468/dl.whqchc
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dama dama",
 "Country is Estonia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015170-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ehnvpw
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gyalidea asteriscus"
 ]
}

 The dataset's 4 records were derived from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015155-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ye3cb
title: Occurrence Download

description: A dataset containing 575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Redigobius bikolanus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 575 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015173-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gfqmcn
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Bangladesh, Nepal, Bhutan, Myanmar, Viet Nam, Lao People’s Democratic Republic, Cambodia, Thailand, Chinese Taipei, Indonesia, Malaysia, Philippines)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Corymbia"
 ]
}

 The dataset includes 127 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015172-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9hctq66
title: Elevation of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia

description: Elevation distribution in the Assateague Island National Seashore (ASIS) salt marsh complex and Chincoteague Bay is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.  References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P92ZW4D9.
DOI: 10.15468/dl.c8ztfm
title: Occurrence Download

description: A dataset containing 631 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myrmotherula ambigua)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 631 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015174-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16744765
title: Connectivity matrix of the potential connectivity (axo-dendritic appositions) of a SSCX model

description: This is a simplified view of the potential synaptic connectivity of layers 2 and 3 of the model deposited under https://zenodo.org/records/8026353. Potential synaptic connectivity means, that all axo-dendritic appositions (or touches) are contained. This is in contrast to the data deposited under 10.5281/zenodo.10079406, where the number of appositions was reduced to a biologically realistic number with a stochastic algorithm based on cooperative synapse formation.

Additionally, the file contains random controls of the connectome: A configuration model control (preserves node degrees) and a distance-dependent control.

The data is in a format that can be loaded using our Connectome_Utilities package. We have converted the internal connectivity of the MICrONS dataset into the same format (found here), allowing researchers to easily run the same analysis scripts on both datasets.

To load:

 - the data: 



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "data")


 - the distance dependent control:



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "dist_dep")


 - the configuration model control:



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "configuration_model")
DOI: 10.5281/zenodo.16744766
title: Connectivity matrix of the potential connectivity (axo-dendritic appositions) of a SSCX model

description: This is a simplified view of the potential synaptic connectivity of layers 2 and 3 of the model deposited under https://zenodo.org/records/8026353. Potential synaptic connectivity means, that all axo-dendritic appositions (or touches) are contained. This is in contrast to the data deposited under 10.5281/zenodo.10079406, where the number of appositions was reduced to a biologically realistic number with a stochastic algorithm based on cooperative synapse formation.

Additionally, the file contains random controls of the connectome: A configuration model control (preserves node degrees) and a distance-dependent control.

The data is in a format that can be loaded using our Connectome_Utilities package. We have converted the internal connectivity of the MICrONS dataset into the same format (found here), allowing researchers to easily run the same analysis scripts on both datasets.

To load:

 - the data: 



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "data")


 - the distance dependent control:



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "dist_dep")


 - the configuration model control:



import conntility

M = conntility.ConnectivityMatrix.from_h5("rat_struc_l23_with_controls.h5", "configuration_model")
DOI: 10.15468/dl.4kra6q
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ADL5660"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015175-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9zzandn
title: Mean tidal range in marsh units of Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia

description: Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Assateague Island National Seashore and Chincoteague Bay based on conceptual marsh units defined by Defne and Ganju (2018). MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) database ( http://vdatum.noaa.gov/ ). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Mean elevation of marsh units is planned to be an underlying parameter in the synthesis of these factors.  References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P92ZW4D9.
DOI: 10.15468/dl.2kxtm5
title: Occurrence Download

description: A dataset containing 3614 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dama dama",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3614 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015176-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hfarsd
title: Occurrence Download

description: A dataset containing 837 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dama dama",
 "Country is Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 837 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015178-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9rcu6k
title: Occurrence Download

description: A dataset containing 278 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pomacea canaliculata",
 "Year 2012-2025"
 ]
}

 The dataset includes 278 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015177-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29821859.v1
title: Raw data for Figures

description: Luciferase assays raw data. These experiments assess how different transcription factors (SOX proteins) influence the activity of the <i>LGALS3</i> gene promoter.
DOI: 10.7910/dvn/nxej0z
title: Replication Data for "The chunk formation and retrieval by second language learners of Chinese"

description: Open materials: Three sets of multi-word expression/word sequences
Open data: Experiment 1 and Experiment 2, as well as data for correlation analysis
DOI: 10.6084/m9.figshare.29821859
title: Raw data for Figures

description: Luciferase assays raw data. These experiments assess how different transcription factors (SOX proteins) influence the activity of the <i>LGALS3</i> gene promoter.
DOI: 10.6084/m9.figshare.29821859.v3
title: Raw data for Figures

description: Luciferase assays raw data. These experiments assess how different transcription factors (SOX proteins) influence the activity of the <i>LGALS3</i> gene promoter.
DOI: 10.6084/m9.figshare.29821859.v2
title: Raw data for Figures

description: Luciferase assays raw data. These experiments assess how different transcription factors (SOX proteins) influence the activity of the <i>LGALS3</i> gene promoter.
DOI: 10.15468/dl.duqdye
title: Occurrence Download

description: A dataset containing 1436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myrmotherula assimilis)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 1436 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015180-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9ow6lfu
title: Unvegetated to vegetated marsh ratio in Plum Island Estuary and Parker River salt marsh complex, Massachusetts

description: Unvegetated to vegetated marsh ratio (UVVR) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex was computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Plum Island Estuary and Parker River salt marsh complex, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. UVVR is planned to be an underlying parameter in the synthesis of these factors.  References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Plum Island Estuary and Parker River salt marsh complex, Massachusetts: U.S. Geological Survey data release, https://doi.org/10.5066/P9XF54QF.
DOI: 10.15468/dl.9zyqjg
title: Occurrence Download

description: A dataset containing 305 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ephippus orbis"
 ]
}

 The dataset includes 305 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015181-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wg3pgq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dama dama",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015182-250802193616735/datasets/export for details.