cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10973877
title: 3DCP.fyi - A Comprehensive Citation Network Graph on the State of the Art in 3D Concrete Printing

description: Research in digital fabrication, specifically in 3D concrete printing (3DCP), has seen a substantial increase in publication output in the past five years, making it hard to keep up with the latest developments. The 3dcp.fyi database aims to provide the research community with a comprehensive, up-to-date, and manually curated literature dataset documenting the development of the field from its early beginnings in the late 1990s to its resurgence in the 2010s until today. The data set is compiled using a systematic approach. A thorough literature search was conducted in scientific databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme. This was then enhanced iteratively with non-indexed literature through a snowball citation search. The authors of the articles were assigned unique and persistent identifiers (ORCID® IDs) through a systematic process that combined querying APIs systematically and manually curating data. The works in the data set also include references to other works, as long as those referenced works are also included within the same data set. A citation network graph is created where scientific articles are represented as vertices, and their citations to other scientific articles are the edges. The constructed network graph is subjected to detailed analysis using specific graph-theoretic algorithms, like PageRank. These algorithms evaluate the structure and connections within the graph, yielding quantitative metrics. Currently, the high-quality dataset contains more than 2600 manually curated scientific works, including journal articles, conference articles, books, and theses, with more than 40000 cross-references and 2000 authors, opening up the possibility for more detailed analysis. The data is published on https://3dcp.fyi, ready for import into several reference managers, and is continuously updated. We encourage researchers to enrich the database by submitting their publications, adding missing works, or suggesting new features.
DOI: 10.5281/zenodo.10973878
title: 3DCP.fyi - A Comprehensive Citation Network Graph on the State of the Art in 3D Concrete Printing

description: Research in digital fabrication, specifically in 3D concrete printing (3DCP), has seen a substantial increase in publication output in the past five years, making it hard to keep up with the latest developments. The 3dcp.fyi database aims to provide the research community with a comprehensive, up-to-date, and manually curated literature dataset documenting the development of the field from its early beginnings in the late 1990s to its resurgence in the 2010s until today. The data set is compiled using a systematic approach. A thorough literature search was conducted in scientific databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme. This was then enhanced iteratively with non-indexed literature through a snowball citation search. The authors of the articles were assigned unique and persistent identifiers (ORCID® IDs) through a systematic process that combined querying APIs systematically and manually curating data. The works in the data set also include references to other works, as long as those referenced works are also included within the same data set. A citation network graph is created where scientific articles are represented as vertices, and their citations to other scientific articles are the edges. The constructed network graph is subjected to detailed analysis using specific graph-theoretic algorithms, like PageRank. These algorithms evaluate the structure and connections within the graph, yielding quantitative metrics. Currently, the high-quality dataset contains more than 2600 manually curated scientific works, including journal articles, conference articles, books, and theses, with more than 40000 cross-references and 2000 authors, opening up the possibility for more detailed analysis. The data is published on https://3dcp.fyi, ready for import into several reference managers, and is continuously updated. We encourage researchers to enrich the database by submitting their publications, adding missing works, or suggesting new features.
DOI: 10.15468/dl.bbcr8c
title: Occurrence Download

description: A dataset containing 239 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ditrichum cornubicum Paton"
 ]
}

 The dataset includes 239 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023629-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.psojkl
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microcystis aeruginosa f. aeruginosa"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004437-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10974026
title: Dataset and code of groudwater nitrate for machine learning

description: Data of groudwater nitrate and related data in North China Plain (NCP). The data including nitrate concentration of groudwater collected from more than 4,000 sites (wells) in NCP from 2005 to 2021. The groundwater samples were collected in 2005–2021, and the collection was conducted in May (before rainy season) and October (after rainy season) in each year for every site.During sampling, basic information about well location, groundwater depth, farmland planting pattern and soil types were collected. Sampling wells were divided into three types according to depth, shallow (0–30 m), medium (30–100 m) and deep (> 100 m). The planting pattern mainly involved intensive croplands, grain crops, vegetable crops and orchards. Soil types of each sampling site were obtained from the China soil database (http://vdb3.soil.csdb.cn/).The socio-economic and agricultural information of the study areas (take the districts of municipalities and prefecture-level cities of provinces as basic units) were acquired via the China Statistical Yearbook (http://www.stats.gov.cn/sj/ndsj/). The data includes agricultural planting area, grain crop area, vegetable planting area, orchard planting area, total facility agricultural area; fertilizer amount, nitrogen fertilizer amount, unit area nitrogen fertilizer amount; total output value of agricultural, forestry, animal and fishery husbandry, agricultural output value, forestry output value, animal husbandry output value, fishery output value; Gross Domestic Product (GDP), per capita GDP; total population, and rural population.
DOI: 10.15468/dl.ctdjj6
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Macromitrium gymnostomum Sull. & Lesq.",
 "Year 1970-2023"
 ]
}

 The dataset includes 70 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031945-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10171393
title: RMLio/rml-ldes-testcases: v1.0.0

description: Test cases for verifying the functionality of RML-LDES integration with IncRML.
DOI: 10.15468/dl.448vh2
title: Occurrence Download

description: A dataset containing 11312 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "TaxonKey is Urocyon cinereoargenteus (Schreber, 1775)"
 ]
}

 The dataset includes 11312 records from 144 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007673-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lrb56j
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tetrastigma leucostaphylum (Dennst.) Alston"
 ]
}

 The dataset includes 189 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004437-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10973689
title: Dataset and code of groudwater nitrate for machine learning

description: Data of groudwater nitrate and related data in North China Plain (NCP). The data including nitrate concentration of groudwater collected from more than 4,000 sites (wells) in NCP from 2005 to 2021. The groundwater samples were collected in 2005–2021, and the collection was conducted in May (before rainy season) and October (after rainy season) in each year for every site.During sampling, basic information about well location, groundwater depth, farmland planting pattern and soil types were collected. Sampling wells were divided into three types according to depth, shallow (0–30 m), medium (30–100 m) and deep (> 100 m). The planting pattern mainly involved intensive croplands, grain crops, vegetable crops and orchards. Soil types of each sampling site were obtained from the China soil database (http://vdb3.soil.csdb.cn/).The socio-economic and agricultural information of the study areas (take the districts of municipalities and prefecture-level cities of provinces as basic units) were acquired via the China Statistical Yearbook (http://www.stats.gov.cn/sj/ndsj/). The data includes agricultural planting area, grain crop area, vegetable planting area, orchard planting area, total facility agricultural area; fertilizer amount, nitrogen fertilizer amount, unit area nitrogen fertilizer amount; total output value of agricultural, forestry, animal and fishery husbandry, agricultural output value, forestry output value, animal husbandry output value, fishery output value; Gross Domestic Product (GDP), per capita GDP; total population, and rural population.
DOI: 10.5281/zenodo.10973818
title: Base de datos de Medicamentos Aprobados por la FDA (2002-2024)

description: Los datos han sido recolectados del sitio web "Drugs.com", específicamente de la página "New Drugs" (https://www.drugs.com/newdrugs.html). Este sitio web es una fuente relevante y confiable de información sobre nuevos medicamentos aprobados por la Administración de Alimentos y Medicamentos (FDA) de los Estados Unidos y otras agencias reguladoras de medicamentos de todo el mundo.
DOI: 10.5281/zenodo.10973889
title: Dataset and code of groundwater nitrate for machine learning

description: Data of groudwater nitrate and related data in North China Plain (NCP). The data including nitrate concentration of groudwater collected from more than 4,000 sites (wells). The groundwater samples were collected in 2005–2021, and the collection was conducted in May (before rainy season) and October (after rainy season) in each year for every site.During sampling, basic information about well location, groundwater depth, farmland planting pattern and soil types were collected. Sampling wells were divided into three types according to depth, shallow (0–30 m), medium (30–100 m) and deep (> 100 m). The planting pattern mainly involved intensive croplands, grain crops, vegetable crops and orchards. Soil types of each sampling site were obtained from the China soil database (http://vdb3.soil.csdb.cn/).The socio-economic and agricultural information of the study areas (take the districts of municipalities and prefecture-level cities of provinces as basic units) were acquired via the China Statistical Yearbook (http://www.stats.gov.cn/sj/ndsj/). The data includes agricultural planting area, grain crop area, vegetable planting area, orchard planting area, total facility agricultural area; fertilizer amount, nitrogen fertilizer amount, unit area nitrogen fertilizer amount; total output value of agricultural, forestry, animal and fishery husbandry, agricultural output value, forestry output value, animal husbandry output value, fishery output value; Gross Domestic Product (GDP), per capita GDP; total population, and rural population. Two scenarios are selected for the machine learning investigations: (i) sampling site information (SSI) and (ii) socio-economic and agricultural information of study area (SEAI). The former is to study the relationship between nitrate data of each sampling site and basic information, while the latter is to study the relationship between average nitrate and socio-economic and agricultural information in the study area. The model selection was a downselection process from well-known machine learning (ML) algorithms to select the best predictive model [50]. Two types of ML algorithms (regression and classification) were used to identify the best emulsion stability prediction model. For the regression prediction of nitrite, 8 commonly used learners were applied: linear regression, LASSO, KNeighbors, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Support Vector (SV), and Multi-Layer Perceptron (MLP) Regressor [51, 52]. For the classification prediction of nitrite, 7 learners were applied: Logistic Regression, KNeighbors, DT, RF, GB, SV, and MLP Classifier. The models were built using the Scikit-Learn machine learning library from Python (https://scikit-learn.org/). The classification was defined according to whether groundwater nitrate exceeded WHO’s maximum contaminant level (10 mg N/L, defined as 1) or not (defined as 0).
DOI: 10.5281/zenodo.7452410
title: Resources of "Declarative RDF Graph Construction: Materialization or Virtualization?"

description: Resources for the paper "Declarative RDF Graph Construction: Materialization or Virtualization?", currently under double-blind review, thus all information about the authors is redacted.



	
GTFS-MADRID-BENCH.tar.xz: GTFS Madrid Bench with scales 1, 25, 50
	
BSBM.tar.xz: BSBM benchmark with scales 5000, 10000, 15000
	
SANTA:
	

		
PARALLEL-LOADING.tar.xz: parallel loading parameter data with 1, 8, 16, 24, 32 cores.
		
DUPLICATES.tar.xz: duplicate values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing duplicates.
		
EMPTY-VALUES.tar.xz: empty values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing empty values.
		
RAW-DATA.tar.xz: scaling among data records & properties parameter data with 1000, 5000, 25000, 125000 records and 1, 5, 10, 15 properties.
		
JOIN-1_1.tar.xz: Join 1-1 data.
		
JOIN-1-N.tar.xz: Join 1-N data with relationship N: 3, 5, 10, 15.
		
JOIN-N-1.tar.xz: Join N-1 data with relationship N: 3, 5, 10, 15.
		
JOIN-N-M.tar.xz: Join N-M data with relationships N-M: 3-3, 3-5, 5-3, 10-5, 5-10.
		
JOIN-DUPLICATES.tar.xz: Join duplicates data with 0%, 25%, 50%, 75%, 100% of the joins generating duplicates.
		
MAPPINGS.tar.xz: Triples Maps & Predicate Object Maps mapping rules impact parameter data with 1 TM + 15 POMs, 15 TMs + 1 POM, 3 TMs + 5 POMs, 5 TMs + 3 POMs.
	
	
	
Graphs
	

		
GTFS-graphs.pdf: GTFS-Madrid-Bench query execution time graph in full resolution
		
BSBM-graphs.pdf: BSBM query execution time graph in full resolution
	
	



Note: These archives are heavily compressed, make sure you have enough disk space to unpack them.


Our bench-executor tool can execute these automatically for you, you need to set the --root parameter to the root directory of the unpacked archive and execute them with the run command or list them all with the list command. Extensive instructions on how to use the tool are available in the README of the linked repository.
DOI: 10.15468/dl.9jc8ae
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lactuca soongarica Regel"
 ]
}

 The dataset includes 32 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137846-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10974154
title: Harvey-Lab-UW/Buonanduci_etal_2024_Ecosphere: Release of data and code for Buonanduci et al. 2024 Ecosphere

description: This is the latest release of data and code for reproducing the analyses in the manuscript 'Few large or many small fires: Using spatial scaling of severe fire to quantify effects of fire-size distribution shifts' by Buonanduci, Donato, Halofsky, Kennedy, and Harvey, published in Ecosphere. See the main text of the manuscript for complete descriptions of how data were processed and analyzed.

This information is licensed under a Creative Commons Attribution 4.0 International License. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.
DOI: 10.5281/zenodo.7452411
title: Resources of "Declarative RDF Graph Construction: Materialization or Virtualization?"

description: Resources for the paper "Declarative RDF Graph Construction: Materialization or Virtualization?", currently under double-blind review, thus all information about the authors is redacted.



	
GTFS-MADRID-BENCH.tar.xz: GTFS Madrid Bench with scales 1, 25, 50
	
BSBM.tar.xz: BSBM benchmark with scales 5000, 10000, 15000
	
SANTA:
	

		
PARALLEL-LOADING.tar.xz: parallel loading parameter data with 1, 8, 16, 24, 32 cores.
		
DUPLICATES.tar.xz: duplicate values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing duplicates.
		
EMPTY-VALUES.tar.xz: empty values parameter data with 0%, 25%, 50%, 75% and 100% of the data containing empty values.
		
RAW-DATA.tar.xz: scaling among data records & properties parameter data with 1000, 5000, 25000, 125000 records and 1, 5, 10, 15 properties.
		
JOIN-1_1.tar.xz: Join 1-1 data.
		
JOIN-1-N.tar.xz: Join 1-N data with relationship N: 3, 5, 10, 15.
		
JOIN-N-1.tar.xz: Join N-1 data with relationship N: 3, 5, 10, 15.
		
JOIN-N-M.tar.xz: Join N-M data with relationships N-M: 3-3, 3-5, 5-3, 10-5, 5-10.
		
JOIN-DUPLICATES.tar.xz: Join duplicates data with 0%, 25%, 50%, 75%, 100% of the joins generating duplicates.
		
MAPPINGS.tar.xz: Triples Maps & Predicate Object Maps mapping rules impact parameter data with 1 TM + 15 POMs, 15 TMs + 1 POM, 3 TMs + 5 POMs, 5 TMs + 3 POMs.
	
	
	
Graphs
	

		
GTFS-graphs.pdf: GTFS-Madrid-Bench query execution time graph in full resolution
		
BSBM-graphs.pdf: BSBM query execution time graph in full resolution
	
	



Note: These archives are heavily compressed, make sure you have enough disk space to unpack them.


Our bench-executor tool can execute these automatically for you, you need to set the --root parameter to the root directory of the unpacked archive and execute them with the run command or list them all with the list command. Extensive instructions on how to use the tool are available in the README of the linked repository.
DOI: 10.15468/dl.drq5ys
title: Occurrence Download

description: A dataset containing 13464 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Human Observation"
 ]
 },
 {
 "or" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Country is Spain",
 "Country is France",
 "Country is Luxembourg",
 "Country is Belgium",
 "Country is Germany",
 "Country is Italy"
 ]
 },
 "Year 1950-2018",
 "HasCoordinate is true",
 "TaxonKey is Rhinolophus ferrumequinum (Schreber, 1774)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 13464 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004437-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbrd42
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cicerbita zhenduoi (S.W.Liu & T.N.Ho) N.Kilian"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137849-230224095556074/datasets/export for details.
DOI: 10.5281/zenodo.10974153
title: Harvey-Lab-UW/Buonanduci_etal_2024_Ecosphere: Release of data and code for Buonanduci et al. 2024 Ecosphere

description: This is the latest release of data and code for reproducing the analyses in the manuscript 'Few large or many small fires: Using spatial scaling of severe fire to quantify effects of fire-size distribution shifts' by Buonanduci, Donato, Halofsky, Kennedy, and Harvey, published in Ecosphere. See the main text of the manuscript for complete descriptions of how data were processed and analyzed.

This information is licensed under a Creative Commons Attribution 4.0 International License. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.
DOI: 10.15468/dl.tcveap
title: Occurrence Download

description: A dataset containing 11437 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-21.48926 17.57813,65.52246 24.60938,36.47461 74.88281,-14.10645 66.88477,-28.6084 67.76367,-33.09082 54.31641,-21.48926 17.57813))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Thymelicus acteon (von Rottemburg, 1775)",
 "Year 1970-2021"
 ]
}

 The dataset includes 11437 records from 218 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017926-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.