cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14971187
title: Collection of Neolithic site coordinates in the Carpathian Basin (LBK, Lengyel I/II and rondels)

description: README

This dataset includes mainly site coordinates and literature used for my master's thesis, submitted at the Institute of Pre- and Protohistoric Archaeology at Kiel University on March 21st, 2025. In Austria, the Czech Republic, Hungary and Slovakia 4275 sites with a rough dating from approx. 5700 to 4400 BC have been collected and used for spatial analysis and modelling (code for analysis at Codeberg.org). Settlement and rondel data are sorted into different .csv files based on their dating or category. All coordinates are in WGS 84/ UTM zone 33N (EPSG: 32633) and Well-Known-Text format. The data is licensed with CC-BY 4.0.

This data collection contains a part of the "Neolithic Settlements in Central Europe: Data from the Project 'Lifestyle as an Unintentional Identity in the Neolithic'" dataset created by Trampota and Pajdla 2021. Also, the data on Hungarian sites is taken from the Hungarian National Database, which does not allow open publishing of coordinates. Therefore the coordinates for Hungarian sites are not included, but can be reconstructed through the provided links in the references. Any researcher can apply very easily for a third level account at the database and get acces to the coordinates.

Data description



id: primary key

site_name: name of the sites

site_type: settlement or rondel

country: country of observation

county: next smaller administrative region

district: next smaller administrative region

muncipality: next smaller administrative region

corridor: field or terrain name

dating: rough (!) dating of sites into LBK or Lengyel, rondels a bit more precise

wkt_32633: coordinate data in WKT EPSG: 32633

coord_qual: quality of recoreded coordinate ranging from 1 (best) to 4 (worst)

approx_dev_m: approximated radius in where the site should be located

arch_measures: recorded archaeological measures done on the site

source: source for data collection, e.g. catalogue

source_site_nr: site number from source

literature: scientific literature on the site
DOI: 10.5281/zenodo.15064330
title: Tables of direct, indirect and networks estimates and Forest plots of pairwise comparisons for all Secondary Outcomes
DOI: 10.5281/zenodo.15064331
title: Tables of direct, indirect and networks estimates and Forest plots of pairwise comparisons for all Secondary Outcomes
DOI: 10.14278/rodare.3587
title: Real-world and OCN river networks

description: This dataset contains two folder with

i) Real-world river networks

ii) OCN river networks

ii) the metrics for i) and ii)

Please see structure of the dataset below:

|-- scalingrivers-data<br>
  |-- Metric results<br>
    |-- metrics_undirected_ocn.csv<br>
    |-- metrics_undirected_real.csv<br>
  |-- OCN networks<br>
    |-- ocn_networks.zip<br>
  |-- River networks<br>
    |-- river_networks.zip<br>
  |-- river_list.csv<br>
DOI: 10.14278/rodare.3633
title: Real-world and OCN river networks

description: The two folders contain the networks at high resolution (HQ) and at a coarser resolution (HS07). Both datasets contain networks from 10 to 1000 nodes approximatly. HQ contains smaller rivers while HS07 contains the major and most relevant rivers.
DOI: 10.14278/rodare.3638
title: Real-world and OCN river networks

description: The two folders contain the networks at high resolution (HR) and at a coarser resolution (HS07). Both datasets contain networks from 10 to 1000 nodes approximatly. HQ contains smaller rivers while HS07 contains the major and most relevant rivers.
DOI: 10.14278/rodare.3586
title: Real-world and OCN river networks

description: The two folders contain the networks at high resolution (HR) and at a coarser resolution (HS07). Both datasets contain networks from 10 to 1000 nodes approximatly. HQ contains smaller rivers while HS07 contains the major and most relevant rivers.
DOI: 10.5281/zenodo.15058841
title: Dataset for the mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with Neural Network modelling within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K

description: Summary:

Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to loading frequencies from 0.1 to 50 Hz, and testing temperatures ranged from 0 to 30 °C. The main scope of this research was to compare analysis between different modelling approaches: conventional regressions, both linear and non-linear, and artificial neural networks.

 

 

The dataset includes:

 

Outcomes of the 4PBT experimental carried out on two types of asphalt concrete: NMAS16 and NMAS22 mixtures



Stiffness Modulus NMAS16.csv

Stiffness Modulus NMAS22.csv
description: This research was conceptualised and developed as part of activities related to project GA22-04047K, funded by The Czech Scientific Foundation (GACR), and project No. 2021/03/Y/ST8/00079, funded by the Polish National Science Centre (NCN) under the Weave-UNISONO 2021. The dataset was used for analyses for the journal paper titled "Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling", which is available at https://doi.org/10.7409/rabdim.025.001
DOI: 10.5281/zenodo.15058842
title: Dataset for the mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with Neural Network modelling within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K

description: Summary:

Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to loading frequencies from 0.1 to 50 Hz, and testing temperatures ranged from 0 to 30 °C. The main scope of this research was to compare analysis between different modelling approaches: conventional regressions, both linear and non-linear, and artificial neural networks.

 

 

The dataset includes:

 

Outcomes of the 4PBT experimental carried out on two types of asphalt concrete: NMAS16 and NMAS22 mixtures



Stiffness Modulus NMAS16.csv

Stiffness Modulus NMAS22.csv
description: This research was conceptualised and developed as part of activities related to project GA22-04047K, funded by The Czech Scientific Foundation (GACR), and project No. 2021/03/Y/ST8/00079, funded by the Polish National Science Centre (NCN) under the Weave-UNISONO 2021. The dataset was used for analyses for the journal paper titled "Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling", which is available at https://doi.org/10.7409/rabdim.025.001
DOI: 10.15468/dl.xsyuhz
title: Occurrence Download

description: A dataset containing 195464 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-4.5 39.8,-3 39.8,-3 41.2,-4.5 41.2,-4.5 39.8))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2020-2025"
 ]
}

 The dataset includes 195464 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020414-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hd3x9t
title: Occurrence Download

description: A dataset containing 5301 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is South Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Aepyceros melampus (Lichtenstein, 1812)"
 ]
}

 The dataset includes 5301 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020421-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25921/s3hw-kd48
title: Rock, bottom water, and sediment sample, environmental, microbial community and carbon data from samples collected from Papahānaumokuākea Marine National Monument on EV Nautilus expeditions NA134 and NA138 (NCEI Accession 0301713)

description: Rock, sediment, and bottom water samples were collected via ROV Hercules from seamounts in the Papahānaumokuākea Marine National Monument during expeditions NA134 and NA138 of EV Nautilus, to examine microbial community diversity and ecosystem services.
DOI: 10.15468/dl.3hyd2n
title: Occurrence Download

description: A dataset containing 1004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dinocampus coccinellae (Schrank, 1802)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1004 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020423-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60797/jae.2025.55.15.3
title: Режим варки целлюлозы для производства целлюлозы для отбелки

description: Journal of Agriculture and Environment, Выпуск 3 (55) 2025
DOI: 10.60797/jae.2025.55.15.5
title: Результаты варки щепы ручного изготовления, полученной из ольхи серой и ивы

description: Journal of Agriculture and Environment, Выпуск 3 (55) 2025
DOI: 10.60797/jae.2025.55.15.6
title: Сравнение показателей целлюлозы из древесины ольхи серой и ивы

description: Journal of Agriculture and Environment, Выпуск 3 (55) 2025
DOI: 10.60797/jae.2025.55.15.7
title: Фракционный состав и результаты анализа волокон на приборе Файбер-тестер после варки и после размола в мельнице ЦРА

description: Journal of Agriculture and Environment, Выпуск 3 (55) 2025
DOI: 10.34770/xm8y-f349
title: Data for "Role of Perturbed Parallel Magnetic Field Effects in Predicting Turbulent Transport in NSTX"

description: The dataset contains data for all figures of Role of Perturbed Parallel
 Magnetic Field Effects in Predicting Turbulent Transport in NSTX submitted
 to PPCF
DOI: 10.15468/dl.gkwdmr
title: Occurrence Download

description: A dataset containing 176236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rosa rugosa Thunb.",
 "Year 2000-2024"
 ]
}

 The dataset includes 176236 records from 315 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020417-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.63qs36
title: Occurrence Download

description: A dataset containing 253220 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-1.61756 36.82093,-2.13465 37.73874,-7.66352 37.83304,-6.66912 35.98859,-5.23718 36.05293,-1.61756 36.82093))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anatidae"
 ]
}

 The dataset includes 253220 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020409-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w9wmfe
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Papaver commutatum Fisch. & C.A.Mey."
 ]
}

 The dataset includes 99 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020427-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/99a2d3a8-1c7d-421e-ac9f-87a2c37bda62
title: CEH Land Cover plus: Pesticides 2012-2017 (England, Scotland and Wales)

description: CEH Land Cover plus: Pesticides maps annual average pesticide applications across England, Wales and Scotland. The product provides application estimates for 162 different active ingredients including herbicides, insecticides, molluscicides and fungicides. It is produced at a 1km resolution with units of kg active ingredient applied per year, averaged between 2012 and 2017. Pesticide application rates (kg/km2/yr) are calculated for each of the crops grown in each 1km square, using information from CEH Land Cover® Plus: Crops 2015, 2016 and 2017 to determine where each crop is grown. Pesticide application data is provided by the Pesticide Usage Survey. Uncertainty maps are produced alongside each active ingredient map to quantify the level of confidence in the estimated applications. Uncertainty is quantified using the distribution of each parameter estimate obtained from the modelling method and is expressed relative to the total application. The product builds upon the Centre for Ecology & Hydrology (CEH) Land Cover® Plus: Crops product.
These maps were created under the NERC funded ASSIST (Achieving Sustainable Agricultural Systems) project to enable exploration of the impacts of agrochemical usage on the environment, enabling farmers and policymakers to implement better, more sustainable agricultural practices.
description: These maps were created under the ASSIST (Achieving Sustainable Agricultural Systems) project by staff at the UK Centre for Ecology & Hydrology to enable exploration of the impacts of agrochemical usage on the environment, enabling farmers and policymakers to implement better, more sustainable agricultural practices.
The maps use data from the Fera Pesticide Utilisation Surveys (PUS), SASA Pesticide Survey Unit and CEH Land Cover® plus: Crops and have been assessed for quality. For further information, please see the supporting documentation available with this data product.
DOI: 10.15468/dl.ce8ejt
title: Occurrence Download

description: A dataset containing 4110 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Salpa thompsoni Foxton, 1961"
 ]
}

 The dataset includes 4110 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014360-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1132785.10fu
title: SH1132785.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132786.10fu
title: SH1132786.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132791.10fu
title: SH1132791.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132794.10fu
title: SH1132794.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132796.10fu
title: SH1132796.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132797.10fu
title: SH1132797.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1132798.10fu
title: SH1132798.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.