cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.fu3u6m
title: Occurrence Download

description: A dataset containing 545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Reaumuria songarica (Pall.) Maxim."
 ]
}

 The dataset includes 545 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011865-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/ftemae
title: Hazara University Herbarium

description: Hazara University is located on the crossroads of the ancient civilization of Gandhara and Ashoka and is on one of the Silk Routes connecting the Indian sub-continent with China and Central Asia. The University was founded in 2001 and has had a strong floristic program since that time. Today, its herbarium includes about 18,000 species and includes all plant groups other than algae. Its primary geographic focus is Northern Pakistan, a mountainous area that includes three major mountain ranges, the Hindu Kush, Karakorum, and Western Himalaya. It started sharing its specimen data via OpenHerbarium and GBIF in 2021.
DOI: 10.15468/dl.nvenus
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Delphinapterus leucas (Pallas, 1776)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011866-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8st9a
title: Occurrence Download

description: A dataset containing 2133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Platanthera micrantha (Hochst. ex Seub.) Schltr."
 ]
}

 The dataset includes 2133 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011867-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.upj2ky
title: Occurrence Download

description: A dataset containing 108302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, United Kingdom of Great Britain and Northern Ireland, Portugal, Spain, Italy, Greece, Svalbard and Jan Mayen, Denmark, Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Belgium, Netherlands, France, Germany, Switzerland, Austria, Türkiye, Russian Federation, Belarus, Ukraine, Romania, Bulgaria, North Macedonia, Albania, Montenegro, Serbia, Kosovo, Slovenia, Bosnia and Herzegovina, Poland, Czechia, Slovakia, Hungary)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Spirodela polyrhiza (L.) Schleid."
 ]
}

 The dataset includes 108302 records from 621 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011863-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbu7yt
title: Occurrence Download

description: A dataset containing 391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zeus faber Linnaeus, 1758",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 391 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011868-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/wvdr9v
title: Castor fiber in the Prioksko-Terrasny Nature Biosphere Reserve (Russia)

description: Castor fiber L. population in the Oka river valley near the medieval city of Serpukhov was extinguished by overhunting in the mid 16th century (Demidov, 2019). Reintroduction started in 1948 on the territory of the newly established Prioksko-Terrasny Reserve. First two pairs were brought from Voronezhsky Reserve (Zablotskaya, 1979) and two more pairs arrived in 1955 from Belorus. Beavers were let go into small rivers - Tadenka and Ponikovka. From the first year of reintroduction and up to the present day the growing population was under constant observation and every beaver settlement was mapped. Number of beavers inhabiting the Reserve is counted after assessment of the settlements’ strength (Lavrov, 1952). The dataset presents the location of all beaver settlements in the Reserve and the number of inhabiting beavers.
DOI: 10.15468/dl.wq88br
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pampus punctatissimus (Temminck & Schlegel, 1845)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 16 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011869-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12328311
title: Addressing the needs of COVID-19 orphans: A systematic review of governmental programs and initiatives

description: The unprecedented COVID-19 pandemic has swept across the globe, claiming lives and livelihoods, overwhelming healthcare systems, disrupting education, and forcing families to grapple with survival. While communities worldwide have risen to the public health challenge, with social workers risking their lives to protect the most vulnerable populations, including children, the social ramifications of the virus's spread have been devastating and rapidly unfolding. The pandemic's ferocity has orphaned countless children across the nation, robbing them of their parents and forcibly separating them from their families. These traumatic circumstances have left these children in a precarious state, compelling them to seek assistance from government agencies or shelter homes for survival.

In response, various governmental bodies have endorsed innovative jurisdictive and programmatic initiatives to mitigate the impact on children. This review provides a comprehensive overview of the supportive schemes and measures implemented in India to aid these bereaved children orphaned by the COVID-19 pandemic. It examines the multifaceted approaches adopted by the government, non-governmental organizations, and international agencies to address the immediate and long-term needs of these vulnerable children, including their emotional, psychosocial, educational, and financial well-being.

The review highlights the collaborative efforts undertaken to ensure the protection, rehabilitation, and reintegration of orphaned children into society, fostering an environment conducive to their growth and development. By synthesizing the existing initiatives and strategies, this review aims to contribute to the ongoing discourse on effective interventions and evidence-based policymaking to safeguard the rights and welfare of children affected by the pandemic's devastating consequences.
DOI: 10.15468/dl.8hr8tz
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyporthodus septemfasciatus (Thunberg, 1793)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 97 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011870-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11656083
title: Addressing the needs of COVID-19 orphans: A systematic review of governmental programs and initiatives

description: The unprecedented COVID-19 pandemic has swept across the globe, claiming lives and livelihoods, overwhelming healthcare systems, disrupting education, and forcing families to grapple with survival. While communities worldwide have risen to the public health challenge, with social workers risking their lives to protect the most vulnerable populations, including children, the social ramifications of the virus's spread have been devastating and rapidly unfolding. The pandemic's ferocity has orphaned countless children across the nation, robbing them of their parents and forcibly separating them from their families. These traumatic circumstances have left these children in a precarious state, compelling them to seek assistance from government agencies or shelter homes for survival.

In response, various governmental bodies have endorsed innovative jurisdictive and programmatic initiatives to mitigate the impact on children. This review provides a comprehensive overview of the supportive schemes and measures implemented in India to aid these bereaved children orphaned by the COVID-19 pandemic. It examines the multifaceted approaches adopted by the government, non-governmental organizations, and international agencies to address the immediate and long-term needs of these vulnerable children, including their emotional, psychosocial, educational, and financial well-being.

The review highlights the collaborative efforts undertaken to ensure the protection, rehabilitation, and reintegration of orphaned children into society, fostering an environment conducive to their growth and development. By synthesizing the existing initiatives and strategies, this review aims to contribute to the ongoing discourse on effective interventions and evidence-based policymaking to safeguard the rights and welfare of children affected by the pandemic's devastating consequences.
DOI: 10.5281/zenodo.11656084
title: Addressing the needs of COVID-19 orphans: A systematic review of governmental programs and initiatives

description: The unprecedented COVID-19 pandemic has swept across the globe, claiming lives and livelihoods, overwhelming healthcare systems, disrupting education, and forcing families to grapple with survival. While communities worldwide have risen to the public health challenge, with social workers risking their lives to protect the most vulnerable populations, including children, the social ramifications of the virus's spread have been devastating and rapidly unfolding. The pandemic's ferocity has orphaned countless children across the nation, robbing them of their parents and forcibly separating them from their families. These traumatic circumstances have left these children in a precarious state, compelling them to seek assistance from government agencies or shelter homes for survival.

In response, various governmental bodies have endorsed innovative jurisdictive and programmatic initiatives to mitigate the impact on children. This review provides a comprehensive overview of the supportive schemes and measures implemented in India to aid these bereaved children orphaned by the COVID-19 pandemic. It examines the multifaceted approaches adopted by the government, non-governmental organizations, and international agencies to address the immediate and long-term needs of these vulnerable children, including their emotional, psychosocial, educational, and financial well-being.

The review highlights the collaborative efforts undertaken to ensure the protection, rehabilitation, and reintegration of orphaned children into society, fostering an environment conducive to their growth and development. By synthesizing the existing initiatives and strategies, this review aims to contribute to the ongoing discourse on effective interventions and evidence-based policymaking to safeguard the rights and welfare of children affected by the pandemic's devastating consequences.
DOI: 10.15468/dl.w6k86z
title: Occurrence Download

description: A dataset containing 1170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "Country is one of (Mexico, Costa Rica, Nicaragua, Guatemala, El Salvador, Belize)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carica papaya L."
 ]
}

 The dataset includes 1170 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011871-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.mz45i48m
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.15468/dl.qugdem
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Wulfila O.P.-Cambridge, 1895"
 ]
}

 The dataset includes 30 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011872-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sqjyyn
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Takifugu porphyreus (Temminck & Schlegel, 1850)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 89 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011873-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10565536
title: TorNet 2019

description: TorNet samples from 2019 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

 

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.10565535
title: TorNet 2019

description: TorNet samples from 2019 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.12655716
title: TorNet 2019

description: TorNet samples from 2019 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.10565581
title: TorNet 2020

description: TorNet samples from 2020 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.10565582
title: TorNet 2020

description: TorNet samples from 2020 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

 

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.12655717
title: TorNet 2020

description: TorNet samples from 2020 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.10565671
title: TorNet 2021

description: TorNet samples from 2021 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

 

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.10565670
title: TorNet 2021

description: TorNet samples from 2021 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
DOI: 10.5281/zenodo.12655718
title: TorNet 2021

description: TorNet samples from 2021 as described in “A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data”

For more detail, visit https://github.com/mit-ll/tornet

 

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

 

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.

 

© 2022 Massachusetts Institute of Technology.

 

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.