cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4m9whvxp8z.1
title: Babitonga bay inlet shorelines and adjacent beaches.

description: This data has the georreferencied vectors of the shoreline position and transect data of the shoreline position evolution of Babitonga bay inlet (Santa Catarina, Brazil).

The shorelines and transects data were obtained by satellite imagery repository analisys using the CASSIE platform (Almeida et al., 2021). They were extracted from Landsat Surface Reflectance imagery to obtain the longest time series available on the platform (1984-2023). The images were previously filtered automatically by the CASSIE algorithm to select only images with cloud cover below 10%. Then, the images were selected by the user to exclude imagens containing clouds close to the mainland/water interface. The baselines were established in parallel to the planform shape of the beach systems, it was draw up in a way that CASSIE calculates the shoreline distance from the baseline using the waterline nearest to the ocean for the cases where the transects intercepts two or more time the identified waterline, as can occurs where the coast has features like sandbars, spits and lagoons. Transects were set up every 50 m along the baseline. The Otsu (1979) threshold used was the automatically suggested by CASSIE, with input equals to zero.

ALMEIDA, L. P., EFRAIM de OLIVEIRA, I., LYRA, R., DAZZI, R. L. S., MARTINS, V. G. & KLEIN, A. H. da F. 2021. Coastal Analyst System from Space Imagery Engine (CASSIE): Shoreline management module. Environmental Modelling & Software, 140(105033). DOI: https://doi.org/10.1016/j.envsoft.2021.105033
DOI: 10.17632/5c5djnrbzn.1
title: Data Collection on Bioinformatics Articles from Web of Science (2022-2024)

description: Description: This dataset includes bibliometric information of 17,810 articles in the field of bioinformatics, collected from Web of Science for the years 2022 to 2024. It contains metadata for each article, such as title, authors, affiliations, publication year, journal name, abstract, keywords, and citation counts. The dataset was utilized for the literature review section of the study titled "Enhancing Scientific Collaboration in Bioinformatics Through Deep Learning and NLP Integration" In Health Informatic Journal. The data supports the identification of key trends, influential works, and significant contributors in the field of bioinformatics. By providing a comprehensive overview of recent research, the dataset aids in understanding the current landscape and informing the study's objectives of improving scientific collaboration through advanced NLP techniques and interdisciplinary efforts.
DOI: 10.17632/hwsf3z853p.1
title: Exposure to dietary glycidyl and 3-MCPD fatty acid esters and associated burden of cancer in selected Asian and European countries: A review and data synthesis

description: In this supplementary file, we used three variables to estimate dietary GE and 3-MCPDE exposure: toxicant concentration in food, mass of food consumed per day, and body weight. We then calculated the cancer risk of dietary GE exposure as years of life lost (YLL) and years lived with disability (YLD). We obtained the average YLL per person (YLLpp) of breast, lung and colorectal cancers for the five-year period by dividing the total YLL of each cancer by its mortality rate. Likewise, we obtained the average YLD per person (YLDpp) of each cancer by dividing the total YLD of each cancer by its prevalence rate. Finally, we computed the dietary GE attributable fraction and rate (per 100,000 persons) of the YLL and YLD of each cancer.
DOI: 10.5281/zenodo.11227589
title: Data from: Trends in habitat quality and habitat degradation within terrestrial protected areas

description: Please see the paper "Trends in habitat quality and habitat degradation within terrestrial protected areas" for the definition of habitat quality, habitat degradation and different threats.

This dataset describes global habitat quality and degradation for 2020, with a resolution of 250 m. Values of habitat quality and total habitat degradation can be used directly. Of note, values of habitat degradation caused by each threat should be multiplied by the relative threat weights before use.  The details are as follows:



"quality_c_2020.tif" represents habitat quality.

"deg_sum_c_2020.tif" represents total habitat degradation which is caused by all 5 threats.

"deg_sum_c_NonIrriCrp_2020.tif" represents habitat degradation caused by non-irrigated cropland, and should be multiplied by 0.2145 before use.

"deg_sum_c_IrriCrp_2020.tif" represents habitat degradation caused by irrigated cropland, and should be multiplied by 0.2292 before use.

"deg_sum_c_CrpVeg_2020.tif" represents habitat degradation caused by mosaic cropland-vegetation, and should be multiplied by 0.1623 before use.

"deg_sum_c_VegCrp_2020.tif" represents habitat degradation caused by mosaic vegetation-cropland, and should be multiplied by 0.0541 before use.

"deg_sum_c_Urban_2020.tif" represents habitat degradation caused by urban areas, and should be multiplied by 0.3400 before use.


Besides, "InputData_forR.zip" provides data on protected-area-level habitat quality and habitat degradation, which is for statistical analyses by R in the paper "Trends in habitat quality and habitat degradation within terrestrial protected areas".
DOI: 10.5281/zenodo.11227590
title: Data from: Trends in habitat quality and habitat degradation within terrestrial protected areas

description: Please see the paper "Trends in habitat quality and habitat degradation within terrestrial protected areas" for the definition of habitat quality, habitat degradation and different threats.

This dataset describes global habitat quality and degradation for 2020, with a resolution of 250 m. Values of habitat quality and total habitat degradation can be used directly. Of note, values of habitat degradation caused by each threat should be multiplied by the relative threat weights before use.  The details are as follows:



"quality_c_2020.tif" represents habitat quality.

"deg_sum_c_2020.tif" represents total habitat degradation which is caused by all 5 threats.

"deg_sum_c_NonIrriCrp_2020.tif" represents habitat degradation caused by non-irrigated cropland, and should be multiplied by 0.2145 before use.

"deg_sum_c_IrriCrp_2020.tif" represents habitat degradation caused by irrigated cropland, and should be multiplied by 0.2292 before use.

"deg_sum_c_CrpVeg_2020.tif" represents habitat degradation caused by mosaic cropland-vegetation, and should be multiplied by 0.1623 before use.

"deg_sum_c_VegCrp_2020.tif" represents habitat degradation caused by mosaic vegetation-cropland, and should be multiplied by 0.0541 before use.

"deg_sum_c_Urban_2020.tif" represents habitat degradation caused by urban areas, and should be multiplied by 0.3400 before use.


Besides, "InputData_forR.zip" provides data on protected-area-level habitat quality and habitat degradation, which is for statistical analyses by R in the paper "Trends in habitat quality and habitat degradation within terrestrial protected areas".
DOI: 10.15468/dl.bafey8
title: Occurrence Download

description: A dataset containing 136928 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oncorhynchus kisutch (Walbaum, 1792))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 136928 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026475-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8363786
title: Evaluation of in vitro genotoxicity tests

description: In  human health risk assessment of chemicals and pharmaceuticals, assessment of genetic toxicity for regulatory purposes usually starts with a standard battery of in vitro genotoxicity tests. This battery is needed to cover the different genetic toxicity endpoints. The tests included in the battery (partly) differ in biology, due to which resulting data may seem contradictory, thereby complicating accurate interpretation of the findings. This could be overcome through application of mathematical modelling. One of the advantages of mathematical modelling is that strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and inform on the associated uncertainties. This approach was explored by the working group “Predictivity of In Vitro Genotoxicity Testing”, formed in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). We applied mathematical modelling to a database with (partly) publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide the strongest weight-of-evidence for predicting genotoxic potential. Remarkably, the Ames bacterial mutation test appeared to contribute very little to these predictions when used in combination with in vitro genetic toxicity tests using cells of mammalian origin. Further research comparing additional in vitro genotoxicity data with in vivo data will provide additional insights  whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.
DOI: 10.5281/zenodo.8363787
title: Evaluation of in vitro genotoxicity tests

description: In  human health risk assessment of chemicals and pharmaceuticals, assessment of genetic toxicity for regulatory purposes usually starts with a standard battery of in vitro genotoxicity tests. This battery is needed to cover the different genetic toxicity endpoints. The tests included in the battery (partly) differ in biology, due to which resulting data may seem contradictory, thereby complicating accurate interpretation of the findings. This could be overcome through application of mathematical modelling. One of the advantages of mathematical modelling is that strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and inform on the associated uncertainties. This approach was explored by the working group “Predictivity of In Vitro Genotoxicity Testing”, formed in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). We applied mathematical modelling to a database with (partly) publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide the strongest weight-of-evidence for predicting genotoxic potential. Remarkably, the Ames bacterial mutation test appeared to contribute very little to these predictions when used in combination with in vitro genetic toxicity tests using cells of mammalian origin. Further research comparing additional in vitro genotoxicity data with in vivo data will provide additional insights  whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.
DOI: 10.15468/dl.u8hbwy
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ataenius brouni (Sharp, 1876))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026519-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.efd94u
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is The New York Botanical Garden Herbarium (NY)",
 "TaxonKey is Hedychium coronarium J.Koenig"
 ]
}

 The dataset includes 118 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026521-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y85n2j
title: Occurrence Download

description: A dataset containing 1263024 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Botanical Database of Southern Africa (BODATSA): Botanical Collections"
 ]
}

 The dataset includes 1263024 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026511-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ksprew
title: Occurrence Download

description: A dataset containing 2173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Geometry POLYGON((-10.64913 34.3084,-15.84399 24.34564,-20.17027 15.38842,-9.54948 3.70839,3.76084 5.48071,12.73445 -7.02496,9.48741 -22.82915,14.92366 -27.32671,15.68326 -35.62727,26.52601 -36.87634,38.53048 -23.54108,46.30505 -26.90807,53.57504 -17.58856,49.58193 -11.49975,43.14022 -10.40865,44.22475 -1.7803,53.53972 10.50201,50.92395 13.67163,43.67487 11.11003,36.71626 21.46022,32.5077 32.11799,32.00411 33.3225,26.7249 32.2417,18.84508 33.74677,15.21115 32.90615,11.60175 34.25812,11.82921 35.56111,10.45347 37.29731,9.29798 37.34526,4.10132 37.73252,-10.64913 34.3084))",
 "TaxonKey is Torgos tracheliotos (J.R.Forster, 1796)",
 "CoordinateUncertaintyInMeters is less than or equal to 40000"
 ]
}

 The dataset includes 2173 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026524-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.up9m9m
title: Occurrence Download

description: A dataset containing 1157 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ornithodoros Koch, 1837"
 ]
}

 The dataset includes 1157 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026520-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnkfjs
title: Occurrence Download

description: A dataset containing 53678 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((38.41227 -6.59194,39.8618 -6.59194,39.8618 -5.53273,38.41227 -5.53273,38.41227 -6.59194))",
 "IucnRedListCategory is one of (LC, NT)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 53678 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026509-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.276p9h
title: Occurrence Download

description: A dataset containing 18485 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ennucula tenuis (Montagu, 1808)"
 ]
}

 The dataset includes 18485 records from 161 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026517-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11235145
title: Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"
DOI: 10.5281/zenodo.11235146
title: Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"
DOI: 10.15468/dl.abv3z8
title: Occurrence Download

description: A dataset containing 3508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Cosmos bipinnatus Cav."
 ]
}

 The dataset includes 3508 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026528-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fsd293
title: Occurrence Download

description: A dataset containing 1080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhyssemus germanus (Linnaeus, 1767))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1080 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026523-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bg6zcf
title: Occurrence Download

description: A dataset containing 2173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Geometry POLYGON((-10.64913 34.3084,-15.84399 24.34564,-20.17027 15.38842,-9.54948 3.70839,3.76084 5.48071,12.73445 -7.02496,9.48741 -22.82915,14.92366 -27.32671,15.68326 -35.62727,26.52601 -36.87634,38.53048 -23.54108,46.30505 -26.90807,53.57504 -17.58856,49.58193 -11.49975,43.14022 -10.40865,44.22475 -1.7803,53.53972 10.50201,50.92395 13.67163,43.67487 11.11003,36.71626 21.46022,32.5077 32.11799,32.00411 33.3225,26.7249 32.2417,18.84508 33.74677,15.21115 32.90615,11.60175 34.25812,11.82921 35.56111,10.45347 37.29731,9.29798 37.34526,4.10132 37.73252,-10.64913 34.3084))",
 "TaxonKey is Torgos tracheliotos (J.R.Forster, 1796)",
 "CoordinateUncertaintyInMeters is less than or equal to 40000"
 ]
}

 The dataset includes 2173 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026530-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8vtk43
title: Occurrence Download

description: A dataset containing 5339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "TaxonKey is Cosmos sulphureus Cav."
 ]
}

 The dataset includes 5339 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026531-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.85rkvk
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Philodendron asplundii Croat & M.L.Soares"
 ]
}

 The dataset's 1 records were derived from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026513-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fcyzd6
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tesarius caelatus (LeConte, 1857))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 56 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026532-240506114902167/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.25868224
title: Microbiota_human_melioidosis

description: This is the study participants' clinical data used for the analyses in "Microbial Resilience in Human Melioidosis: Understanding Gut Microbiome and Resistome Dynamics in Diagnosis to Recovery" (submitted). For questions: Soumi Chowdhury at soumi.sia1995@gmail.com (first author) or Chiranjay Mukhopadhyay at chiranjay.m@manipal.edu (corresponding author).
DOI: 10.6084/m9.figshare.25868224.v1
title: Microbiota_human_melioidosis

description: This is the study participants' clinical data used for the analyses in "Microbial Resilience in Human Melioidosis: Understanding Gut Microbiome and Resistome Dynamics in Diagnosis to Recovery" (submitted). For questions: Soumi Chowdhury at soumi.sia1995@gmail.com (first author) or Chiranjay Mukhopadhyay at chiranjay.m@manipal.edu (corresponding author).
DOI: 10.15468/dl.9zqyfr
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tesarius mcclayi (Cartwright, 1955))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 61 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026534-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pp7fzk
title: Occurrence Download

description: A dataset containing 61113 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is UTA Amphibians"
 ]
}

 The dataset includes 61113 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026485-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11235156
title: CEOS LPV DIRECT V2.1: A database of upscaled LAI, FAPAR and Fcover values for satellite biophysical product validation

description: Ground references of high quality are needed to validate satellite-based products. The DIRECT V2.1 database compiles Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FCover) averaged values over a 3 km x 3 km area. The ground data was upscaled using high spatial resolution imagery following CEOS WGCV LPV (so called CEOS LPV) LAI validation good practices (Fernandes et al., 2014) to properly account for the spatial heterogeneity of the site. Ground measurements performed during several international Cal/Val activities, including VALERI, BigFoot, SAFARI-2000, CCRS, Boston University, were compiled by S. Garrigues (Garrigues et al., 2008) in the DIRECT database, and later ingested in the CEOS LPV OLIVE tool (Weiss et al., 2014) for accuracy assessment.

F. Camacho reviewed DIRECT to remove those sites without understory measurements (Camacho et al., 2013) and after that expanded the database adding the ImagineS network of sites (Camacho et al., 2021). DIRECT V2.1 is the last update including 44 new sites from China (Fang et al., 2019; Song et al., 2021) and 2 more sites from ESA FRM4Veg project (Brown et al., 2021).

The CEOS LPV DIRECT V2.1 database constitutes a major effort of the international community to provide ground reference for the validation of satellite-based LAI and FAPAR ECVs, with a total of 176 sites around the world (7 main biome types) and 280 LAI values, 128 FAPAR and 122 FCOVER values covering the period from 2000 to 2021.

 

Data description



LAI, LAIeff, FAPAR and FCOVER upscaled values over 3 km x 3 km.

LAI_NoUnderstory, refers to sites where only overstory was measured and thus are not recommended for accuracy assessment of satellite products.


File contents

A Header " Sites":

General information for each site (coordinates, landcover, method, reference)

For each variable:



# number of the site (site description in Sites)

Lat_cen, latitude centre of 3km x 3km

Lon_cen, longitude centre of 3km x 3km

Site name, name of the site

Date, MM/DD/YYYY

Mean, average value over 3km x 3km

Uncert, uncertainty over 3 km x 3km (STD)