cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29136992.v3
title: <b>Data and code from: The effects of climate change on European distributions of four alien marine crab species</b>

description: The rate of marine species introductions in the European Union (EU) continues to increase, with climate change facilitating their spread and impact. Crabs and other crustaceans are among the most successful groups of marine invasives, they are well-adapted to thrive in novel environments and can have significant negative ecological and economic impact where they become established. To effectively assess the ecological and economic threats posed by these species, and to support potential mitigation strategies, it is important to be able to determine current distributions, and to predict the potential for further range expansion under expected climate scenarios. We studied the current distribution and future distribution under various climate change scenarios of four predatory brachyuran crabs that were previously identified as species of concern for European seas namely, <i>Portunus segnis</i>, <i>Charybdis longicollis,</i> <i>Hemigrapsus sanguineus</i>, and <i>Matuta victor</i>. Species distribution models were built using an ensemble modelling approach. The results show that the potential distributions for all species are much larger than the current known distributions. Under all predicted climate change scenarios, the climatic conditions for <i>P. segnis</i>, <i>C. longicollis, </i>and <i>M. victor</i>, in particular, are expected improve in most of the Mediterranean sea, resulting in an expansion of suitable habitat. The Adriatic and Aegean seas are of particular concern as the results show that these seas are not only highly suitable under current climatic conditions but that these will become more suitable under all climate scenarios. It is, therefore, important to further investigate potential impacts, to increase monitoring, and explore possible management strategies for these seas in order to manage the invasion of these species and avoid future biodiversity and economic losses.
DOI: 10.6084/m9.figshare.29136992.v2
title: <b>Data from: The effects of climate change on European distributions of four alien marine crab species</b>

description: The rate of marine species introductions in the European Union (EU) continues to increase, with climate change facilitating their spread and impact. Crabs and other crustaceans are among the most successful groups of marine invasives, they are well-adapted to thrive in novel environments and can have significant negative ecological and economic impact where they become established. To effectively assess the ecological and economic threats posed by these species, and to support potential mitigation strategies, it is important to be able to determine current distributions, and to predict the potential for further range expansion under expected climate scenarios. We studied the current distribution and future distribution under various climate change scenarios of four predatory brachyuran crabs that were previously identified as species of concern for European seas namely, <i>Portunus segnis</i>, <i>Charybdis longicollis,</i> <i>Hemigrapsus sanguineus</i>, and <i>Matuta victor</i>. Species distribution models were built using an ensemble modelling approach. The results show that the potential distributions for all species are much larger than the current known distributions. Under all predicted climate change scenarios, the climatic conditions for <i>P. segnis</i>, <i>C. longicollis, </i>and <i>M. victor</i>, in particular, are expected improve in most of the Mediterranean sea, resulting in an expansion of suitable habitat. The Adriatic and Aegean seas are of particular concern as the results show that these seas are not only highly suitable under current climatic conditions but that these will become more suitable under all climate scenarios. It is, therefore, important to further investigate potential impacts, to increase monitoring, and explore possible management strategies for these seas in order to manage the invasion of these species and avoid future biodiversity and economic losses.
DOI: 10.15468/dl.jffnsx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030820-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ta5nyz
title: Occurrence Download

description: A dataset containing 4453 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, South America)",
 "OccurrenceStatus is Present",
 "TaxonKey is Hybognathus nuchalis Agassiz, 1855"
 ]
}

 The dataset includes 4453 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030821-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eed2tu
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Chusquea erecta L.G.Clark"
 ]
}

 The dataset includes 17 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030822-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4q3p9w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1586 -33.5889,28.2048 -32.9007,25.5395 -32.9007,25.5395 -34.1098,25.6468 -34.1222,25.7787 -34.0734,25.7098 -33.9109,25.8639 -33.797,26.4806 -33.8473,27.1586 -33.5889))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030823-250525065834625/datasets/export for details.
DOI: 10.15468/dl.bmbrq9
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Cervus mariannus Desmarest, 1822"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030826-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkn4zp
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Chusquea fasciculata Döll"
 ]
}

 The dataset includes 21 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030825-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7an2zn
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Cervus mariannus Desmarest, 1822"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030824-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12108879
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.10633788
title: # Replication code and data for: Tracking green space along streets of world cities

description: # Replication code and data for: Tracking green space along streets of world citiesBy Giacomo Falchetta and Ahmed T. HammadPreprint: https://doi.org/10.21203/rs.3.rs-3916891/v1

To replicate the analysis, the results, and the figures of the paper:



Download input data from this Zenodo repository

*Optional steps* (processed output data are already available in this Zenodo repository):



Run [lines 1-7] of workflow/sourcer.R

Run the .js scripts in Google Earth Engine (training_data_sentinel_gee.js, training_data_era5_gee.js, training_data_lc_gee.js, training_data_pop_gee.js, prediction_data_gee.js) and complete the export to Drive tasks to generate the output .csv files



Run workflow/sourcer.R [from line 9-37] to train the ML model and make predictions (including figures and tables replication)


______

To make on-demand urban green space GVI estimation for user-inputed coordinate points:



Configure the rgee package on your local computer

Update your Google Earth Engine-enabled email in [line 7] of the script workflow/on_demand_gvi_estimator.R

Define your custom set of coordinate points in [line 41] of the script workflow/sourcer.R

Run workflow/sourcer.R [line 42] to make on-demand urban green space GVI estimation and visualize results
DOI: 10.5281/zenodo.12504988
title: # Replication code and data for: Tracking green space along streets of world cities

description: # Replication code and data for: Tracking green space along streets of world citiesBy Giacomo Falchetta and Ahmed T. HammadPreprint: https://doi.org/10.21203/rs.3.rs-3916891/v1

To replicate the analysis, the results, and the figures of the paper:



Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking

Optional steps (processed output data are already available in this repository):



Adjust your working directory

Run [lines 4-11] of workflow/sourcer.R

Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine and complete the export to Drive tasks to generate the output .csv files



Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)
DOI: 10.5281/zenodo.13886667
title: # Replication code and data for: Tracking green space along streets of world cities

description: *CHECK THE UPDATED VERSION CONTAINING OUTPUT DATA FOR 358 WORLD CITIES*

https://zenodo.org/records/15624204

 

####

# Replication code and data for: Tracking green space along streets of world citiesFalchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 

To replicate the analysis, the results, and the figures of the paper:



Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking

*Optional data extraction steps* (processed output data are already available in the Zenodo repository):



Adjust your working directory

Run [lines 4-11] of workflow/sourcer.R

Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com)  and complete the export to Drive tasks to generate the output .csv files



Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)
DOI: 10.5281/zenodo.15624204
title: # Replication code and data for: Tracking green space along streets of world cities

description: # Replication code and data for: Tracking green space along streets of world citiesFalchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 

The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains output data, reporting sampling-point level data on the yearly (2016-2023) values of the  Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units.   

____

To replicate the analysis, the results, and the figures of the paper:



Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking

*Optional data extraction steps* (processed output data are already available in the Zenodo repository):



Adjust your working directory

Run [lines 4-11] of workflow/sourcer.R

Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com)  and complete the export to Drive tasks to generate the output .csv files



Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)
DOI: 10.5281/zenodo.8001676
title: # Replication code and data for: Tracking green space along streets of world cities

description: # Replication code and data for: Tracking green space along streets of world citiesFalchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 

The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains output data, reporting sampling-point level data on the yearly (2016-2023) values of the  Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units.   

____

To replicate the analysis, the results, and the figures of the paper:



Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking

*Optional data extraction steps* (processed output data are already available in the Zenodo repository):



Adjust your working directory

Run [lines 4-11] of workflow/sourcer.R

Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com)  and complete the export to Drive tasks to generate the output .csv files



Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)
DOI: 10.5281/zenodo.8001677
title: # Replication code and data for: Global urban green space is shrinking

description: # Replication code and data for: Global urban green space is shrinking<br> By Giacomo Falchetta and Ahmed T. Hammad To replicate the analysis: Download input data from https://doi.org/10.5281/zenodo.8001677 <strong>*Optional steps*</strong> (processed output data are already available in this Zenodo repository): Run [lines 1-5] of <code>workflow/sourcer.R</code> Run the <em>.js</em> scripts in Google Earth Engine (<code>training_data_sentinel_gee.js, training_data_era5_gee.js, training_data_lc_gee.js, training_data_pop_gee.js, prediction_data_gee.js</code>) and complete the export to Drive tasks to generate the output .csv files Run <code>workflow/sourcer.R</code> [lines 9-31] to train the ML model and make predictions (including figures and tables replication) To make on-demand urban green space GVI estimation for user-inputed coordinate points: Configure the rgee package on your local computer Update your Google Earth Engine-enabled email in [line 7] of the script <em>workflow/on_demand_gvi_estimator.R</em> Define your custom set of coordinate points in [line 35] of the script <em>workflow/sourcer.R</em> Run <em>workflow/sourcer.R</em> [line 36] to make on-demand urban green space GVI estimation and visualize results Manuscript under peer review.
DOI: 10.15468/dl.rv3bj7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030827-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.13326962
title: Turkey23_backward_branching
DOI: 10.5281/zenodo.13326963
title: Turkey23_to_YZ
DOI: 10.5281/zenodo.15622230
title: Turkey23_backward_branching
DOI: 10.15468/dl.rdgrkx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030829-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.28430546.v2
title: Antiviral results against bacteriophage phi 6, bacteriophage MS2 and enterovirus EV71

description: Antiviral properties of several materials against bacteriophage phi 6 (an enveloped virus) and bacteriophage MS2 (a non-enveloped virus), and against enterovirus EV71.
DOI: 10.5281/zenodo.15624541
title: Royal Society Corpus - Parsing evaluation

description: Selection of sentences from the Royal Society Corpus (RSC) (https://fedora.clarin-d.uni-saarland.de/rsc_v6/) used for evaluating dependency parsing with the Stanza tool.

Twenty sentences from each period of the RSC were randomly selected and manually corrected by two annotators.

The evaluation is described in the paper: "Tracing Syntactic Complexity: Exploring the Evolution of Average Dependency Length Across Three Centuries of Scientific English" - SyntaxFest 2025.
DOI: 10.5281/zenodo.15624542
title: Royal Society Corpus - Parsing evaluation

description: Selection of sentences from the Royal Society Corpus (RSC) (https://fedora.clarin-d.uni-saarland.de/rsc_v6/) used for evaluating dependency parsing with the Stanza tool.

Twenty sentences from each period of the RSC were randomly selected and manually corrected by two annotators.

The evaluation is described in the paper: "Tracing Syntactic Complexity: Exploring the Evolution of Average Dependency Length Across Three Centuries of Scientific English" - SyntaxFest 2025.
DOI: 10.15468/dl.3p47k3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030831-250525065834625/datasets/export for details.
DOI: 10.15468/dl.qj2czf
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Paradoxurus philippinensis Jourdan, 1837"
 ]
}

 The dataset includes 177 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030833-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29298y
title: Occurrence Download

description: A dataset containing 4453 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, South America)",
 "OccurrenceStatus is Present",
 "TaxonKey is Hybognathus nuchalis Agassiz, 1855"
 ]
}

 The dataset includes 4453 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030835-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k852w6
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Paradoxurus philippinensis Jourdan, 1837"
 ]
}

 The dataset includes 177 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030834-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7svagt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arthroleptella atermina Turner & Channing, 2017",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030836-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15624622
title: Supplementary information for A novel systematic study of the Early Eocene bivalves from the palaeoequatorial Cambay Basin, Gujarat, India