cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.q5nsey
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megangulus zyonoensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019449-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yrbjq9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tor putitora",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019451-250914085247600/datasets/export for details.
DOI: 10.3886/e238118
title: Business Improvement Districts

description: Business Improvement Districts (BID). The dataset contains locations and attributes of the BID jurisdictions (Downtown, Golden Triangle, Mt. Vernon Triangle, Adams Morgan, Georgetown, and NoMa), first authorized under the Business Improvement Districts Act of 1996. This layer also contains the Mt. Vernon Community Improvement District (CID). Jurisdictions were identified from public records (websites, legacy GIS data, real property records).
DOI: 10.3886/e238118v1
title: Business Improvement Districts

description: Business Improvement Districts (BID). The dataset contains locations and attributes of the BID jurisdictions (Downtown, Golden Triangle, Mt. Vernon Triangle, Adams Morgan, Georgetown, and NoMa), first authorized under the Business Improvement Districts Act of 1996. This layer also contains the Mt. Vernon Community Improvement District (CID). Jurisdictions were identified from public records (websites, legacy GIS data, real property records).
DOI: 10.15468/dl.ncuert
title: Occurrence Download

description: A dataset containing 1610786 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is NZ National Vegetation Survey occurrence data"
 ]
}

 The dataset includes 1610786 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019420-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pbuj9q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Metteniusales)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1981",
 "Geometry POLYGON((-30 25,50 25,50 75,-30 75,-30 25))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019452-250914085247600/datasets/export for details.
DOI: 10.5281/zenodo.17161378
title: Machine learning identifies predictors of amniotic membrane graft failure in neurotrophic keratitis

description: Abstract

Introduction

Neurotrophic keratitis (NK) is a rare, vision-threatening corneal disease characterized by impaired epithelial healing and frequent recurrence of defects. Amniotic membrane transplantation (AMT) is an established therapeutic option, but outcomes remain variable and predictors of failure are poorly defined. We aimed to identify clinical and systemic factors associated with recurrence following AMT using both classical statistics and machine learning approaches.

Methods

We retrospectively analyzed 66 patients with NK who underwent AMT between 2019 and 2025 at a tertiary referral center. The primary outcome was epithelial defect recurrence following AMT, and the a priori primary model was a multivariable logistic regression complemented by machine learning approaches. A total of 66 patients had complete data for analysis; missing data were minimal and addressed using complete-case analysis. Clinical, demographic, and ophthalmological variables were retrospectively collected. Predictors of recurrence were assessed using bivariate tests, multivariable regression, principal component analysis (PCA), hierarchical clustering, and random forest modeling.

Results

Epithelial defect recurrence occurred in 46 patients (70%). Multivariable analysis identified systemic immunosuppressive therapy (odds ratio [OR] 19.9; p = 0.023) and number of AMTs (OR 2.73 per graft; p = 0.040) as independent predictors, with prior ocular surgery showing a borderline association (p = 0.060). PCA and clustering revealed three phenotypic subgroups, including a high-risk cluster (72% recurrence) characterized by immunosuppression, multiple AMTs, prior surgery, and inflammatory complications. Random forest classification confirmed the predictive role of these variables, achieving an AUC of 0.82 with balanced sensitivity (74%) and specificity (81%).

Conclusion

Systemic immunosuppression, repeated AMTs, and prior ocular surgery are key predictors of AMT failure in NK. Combining regression models, clustering, and machine learning provides a robust framework for risk stratification. These findings support the development of personalized monitoring and treatment strategies to improve surgical outcomes in NK.
DOI: 10.5281/zenodo.17161377
title: Machine learning identifies predictors of amniotic membrane graft failure in neurotrophic keratitis

description: Abstract

Introduction

Neurotrophic keratitis (NK) is a rare, vision-threatening corneal disease characterized by impaired epithelial healing and frequent recurrence of defects. Amniotic membrane transplantation (AMT) is an established therapeutic option, but outcomes remain variable and predictors of failure are poorly defined. We aimed to identify clinical and systemic factors associated with recurrence following AMT using both classical statistics and machine learning approaches.

Methods

We retrospectively analyzed 66 patients with NK who underwent AMT between 2019 and 2025 at a tertiary referral center. The primary outcome was epithelial defect recurrence following AMT, and the a priori primary model was a multivariable logistic regression complemented by machine learning approaches. A total of 66 patients had complete data for analysis; missing data were minimal and addressed using complete-case analysis. Clinical, demographic, and ophthalmological variables were retrospectively collected. Predictors of recurrence were assessed using bivariate tests, multivariable regression, principal component analysis (PCA), hierarchical clustering, and random forest modeling.

Results

Epithelial defect recurrence occurred in 46 patients (70%). Multivariable analysis identified systemic immunosuppressive therapy (odds ratio [OR] 19.9; p = 0.023) and number of AMTs (OR 2.73 per graft; p = 0.040) as independent predictors, with prior ocular surgery showing a borderline association (p = 0.060). PCA and clustering revealed three phenotypic subgroups, including a high-risk cluster (72% recurrence) characterized by immunosuppression, multiple AMTs, prior surgery, and inflammatory complications. Random forest classification confirmed the predictive role of these variables, achieving an AUC of 0.82 with balanced sensitivity (74%) and specificity (81%).

Conclusion

Systemic immunosuppression, repeated AMTs, and prior ocular surgery are key predictors of AMT failure in NK. Combining regression models, clustering, and machine learning provides a robust framework for risk stratification. These findings support the development of personalized monitoring and treatment strategies to improve surgical outcomes in NK.
DOI: 10.7910/dvn/nze8qy
title: Revitalisation of intermittent preventive treatment of malaria in pregnancy in Kenya (Revive IPTp)

description: This was a repeated cross-sectional survey that aimed to support the Government of Kenya’s National Malaria Control Program goal of closing the gap towards the achievement of the national target of 80% coverage of IPTp-SP by 2025. Specifically, this project sought to:
1. Increase programme implementers and healthcare providers' understanding, capacity and willingness to promote use of SP in IPTp;
2. Increase community engagement on IPTp and promote self-care by pregnant women; and,
3. Strengthen the capacity of county government bodies to deliver and monitor IPTp-SP interventions as part of the country’s decentralised healthcare system.
DOI: 10.15468/dl.sj6xrh
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amborellales)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1981",
 "Geometry POLYGON((-30 25,50 25,50 75,-30 75,-30 25))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019453-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pup4ym
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protomyzon whiteheadi)",
 "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 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019454-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8snk57
title: Occurrence Download

description: A dataset containing 708 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moerella pulchella",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 708 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019455-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8vgnzx
title: Occurrence Download

description: A dataset containing 191243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pycnonotus leucotis, Phenacoccus manihoti, Heteropsylla cubana, Chilo partellus, Allium neapolitanum, Callisia fragrans, Dahlia imperialis, Euryops chrysanthemoides, Acacia colei, Acrocarpus fraxinifolius, Desmodium uncinatum, Tephrosia vogelii, Passiflora subpeltata, Dovyalis caffra, Crassula sarmentosa, Kalanchoe fedtschenkoi, Cestrum laevigatum, Rattus tanezumi, Calyptocarpus vialis, Pluchea carolinensis)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 191243 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019402-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e238116
title: ACS 1-Year Economic Characteristics DC

description: Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.
DOI: 10.15468/dl.m9ek7r
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chanos chanos",
 "Country is Bangladesh",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019456-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3uuu87
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chanos chanos",
 "Country is Cook Islands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019457-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e238116v1
title: ACS 1-Year Economic Characteristics DC

description: Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.
DOI: 10.15468/dl.ygdmh8
title: Occurrence Download

description: A dataset containing 121 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chanos chanos",
 "Country is Philippines",
 "HasCoordinate is true"
 ]
}

 The dataset includes 121 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019458-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17137241
title: PVT1 lincRNA signals an androgen-dependet transcriptional activation program of oncogenes in prostate cancer cells

description: The results of signals obtained with CUT&RUN for histone marks (H3K4me1,

H3K4me3, H3K27ac and H3K27me3), EZH2 and AR occupancy along the

human genome are available here. Briefly, prostate cancer LNCaP cells were 

treated with androgen analog R1881 or with solvent (EtOH), with or without 

PVT1 knockdown.  The names of the files describe the type of histone mark 

or protein studied, if there is PVT1 knockdown (sg134) or not (ntc), and 

treatment with R1881 or EtOH. For instance, the file AR_sg134_R1881_r1.bw 

corresponds to the bigWig file of a CUT&RUN assay with anti-AR antibody

in cells with PVT1 knockdown treated with androgen analog (R1881) and 

replicate 1.

 

 
description: To upload these tracks:

 

go to UCSC genome browser (https://genome.ucsc.edu/) and use the human genome assembly (hg38).

Click at "My Data" >> "Custom Tracks"

There are two different ways to upload your data:

 

Copy the URL link of the chosen file into the white box and click submit (it can take a few minutes to upload)

        OR

Download the file to your computer 

Click at "Choose File" and upload your downloaded file

Click submit
DOI: 10.15468/dl.hvm7vb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Paracryphiales)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1981",
 "Geometry POLYGON((-30 25,50 25,50 75,-30 75,-30 25))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019459-250914085247600/datasets/export for details.
DOI: 10.3886/e238122
title: Zoning Cases

description: About the BZA - The Board of Zoning Adjustment (BZA) is an independent, quasi-judicial body. It is empowered to grant relief from the strict application of the Zoning Regulations (variances), approve certain uses of land (special exceptions), and hear appeals of actions taken by the Zoning Administrator at DCRA. The Board's five members consist of three Mayoral appointees, a rotating member of the District of Columbia Zoning Commission, and a designated representative of the National Capital Planning Commission. In cases where the Board is performing functions regarding Foreign Mission and Chancery applications, the composition of the Board shall consist of the three Mayoral appointees, the Director of the U.S. National Park Service or his designee, and the Executive Director of the National Capital Planning Commission. About the ZC - The Zoning Commission (ZC) is an independent, quasi-judicial body. Created by the Zoning Act of 1920, as amended, the ZC is charged with preparing, adopting, and subsequently amending the Zoning Regulations and Zoning Map in a means not inconsistent with the Comprehensive Plan for the National Capital area. Three members of the ZC are residents of the District of Columbia appointed by the Mayor and confirmed by the Council. The fourth member of the ZC is the Architect of the Capitol (or his/her representative). The fifth ZC member is the Director of the National Park Service (or his/her representative).
DOI: 10.15468/dl.xy674u
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Omala hyalina",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019462-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e238122v1
title: Zoning Cases

description: About the BZA - The Board of Zoning Adjustment (BZA) is an independent, quasi-judicial body. It is empowered to grant relief from the strict application of the Zoning Regulations (variances), approve certain uses of land (special exceptions), and hear appeals of actions taken by the Zoning Administrator at DCRA. The Board's five members consist of three Mayoral appointees, a rotating member of the District of Columbia Zoning Commission, and a designated representative of the National Capital Planning Commission. In cases where the Board is performing functions regarding Foreign Mission and Chancery applications, the composition of the Board shall consist of the three Mayoral appointees, the Director of the U.S. National Park Service or his designee, and the Executive Director of the National Capital Planning Commission. About the ZC - The Zoning Commission (ZC) is an independent, quasi-judicial body. Created by the Zoning Act of 1920, as amended, the ZC is charged with preparing, adopting, and subsequently amending the Zoning Regulations and Zoning Map in a means not inconsistent with the Comprehensive Plan for the National Capital area. Three members of the ZC are residents of the District of Columbia appointed by the Mayor and confirmed by the Council. The fourth member of the ZC is the Architect of the Capitol (or his/her representative). The fifth ZC member is the Director of the National Park Service (or his/her representative).