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

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Steindachneridion melanodermatum Garavello, 2005"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000921-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pw8lfg
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Renealmia jamaicensis (Gaertn.) Horan.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 77 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000121-160802083343578/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbqpyu
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amietophrynus mauritanicus (Schlegel, 1841)"
 ]
}

 The dataset includes 52 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021891-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.khnvaz
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Proscylliidae",
 "Continent is one of (North America, South America)"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059572-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gxoxcm
title: Occurrence Download

description: A dataset containing 192 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 3) Planktonic foraminifer species distribution, abundance, and preservation in DSDP Hole 92-598"
 ]
}

 The dataset includes 192 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000121-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.juc6yz
title: Occurrence Download

description: A dataset containing 449536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 {
 "or" : [
 "Country is New Zealand",
 "Country is Australia",
 "Country is Papua New Guinea",
 "Country is Indonesia",
 "Country is Singapore"
 ]
 },
 "Year 1999-2019",
 "TaxonKey is Trichoglossus haematodus (Linnaeus, 1771)"
 ]
}

 The dataset includes 449536 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021889-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzem8g
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlamydoselachidae",
 "Continent is one of (North America, South America)"
 ]
}

 The dataset includes 42 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059586-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mbg11n
title: Occurrence Download

description: A dataset containing 225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio L.",
 "Geometry POLYGON((-168.99 53.89,-168.99 63.01,-141.44 63.01,-141.44 53.89,-168.99 53.89))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 225 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000121-170814131059671/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ze4gvf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is one of (Fusinus australis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0188062-230224095556074/datasets/export for details.
DOI: 10.15468/dl.dwkqpe
title: Occurrence Download

description: A dataset containing 496 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prunus amygdalus Batsch"
 ]
}

 The dataset includes 496 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021894-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.22967726
title: Grain Size of Wax Lake Delta Island Cores

description: Data are grain size measurements collected from cores taken along cross sections of an island on Wax Lake Delta, LA during the summer of 2015. Sediment samples were taken from cores at constant depth invervals.Grain size was processed after sediment samples were treated with H2O2 to remove organic matter. Grain size distributions were measured using laser grain diffration instrument (Mastersizer).
DOI: 10.7294/22903964
title: A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides

description: Datasheets used in SiOC machine learning work.
DOI: 10.7294/22903964.v1
title: A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides

description: Datasheets used in SiOC machine learning work.
DOI: 10.6084/m9.figshare.22958861.v2
title: Datasets for "Quantitative Assessment of Grasping Strength in Platyrrhine Monkeys"

description: Data associated with the manuscript "Quantitative Assessment of Grasping Strength in Platyrrhine Monkeys" by Young et al. Data include in vivo grasping force data from squirrel monkeys and marmosets (Study1_GraspingForceData.xlsx), digital flexor muscle physiological cross-sectional data from a variety of platyrrhines (Study2_MuscleData.xlsx), and the phylogenetic tree of the platyrrhines for which muscle data were collected (Study2_PlatyrrhineTree.nex). <br>
DOI: 10.6084/m9.figshare.22958861
title: Datasets for "Quantitative Assessment of Grasping Strength in Platyrrhine Monkeys"

description: Data associated with the manuscript "Quantitative Assessment of Grasping Strength in Platyrrhine Monkeys" by Young et al. Data include in vivo grasping force data from squirrel monkeys and marmosets (Study1_GraspingForceData.xlsx), digital flexor muscle physiological cross-sectional data from a variety of platyrrhines (Study2_MuscleData.xlsx), and the phylogenetic tree of the platyrrhines for which muscle data were collected (Study2_PlatyrrhineTree.nex). <br>
DOI: 10.5281/zenodo.7377044
title: Data for: Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour

description: These datasets contain supplementary material for the article " Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour " accepted to Fire Safety Journal on April 16, 2023 (DOI 10.1016/j.firesaf.2023.103797). The article presents experimental data sets on the evacuation movement and behaviour of preschool children observed during 15 evacuation drills in 10 nursery schools in the Czech Republic involving 970 children (3-7 years of age) and 87 staff members. In the presented spreadsheets, raw experimental data on speed-density and flow-density relationships are provided separately for corridors, straight staircases (flights, landings, and entire staircase), and doorways. The movement travel speed ('Speed') is expressed in [m·s<sup>-1</sup>], specific flow ('Flow') in [pers·s<sup>-1</sup>·m<sup>-1</sup>], density variable is expressed in the units of [pers·m<sup>−2</sup>] ('Density1') and [m<sup>−2</sup>·m<sup>−2</sup>] ('Density2'). Observations made for the different age groups of children are distinguished by letters: 'J' – Junior, 'S' – Senior, 'S+' - Senior+, 'M' – Mixed. In speed-density data sets, observations for walking children are denoted as 'W', for running children as 'R' (e.g., 'JW' – Junior walking). Speed-density data points that were calculated by excluding waiting times of children (only in the spreadsheets for corridors and landings of straight staircases) are marked 'M' after age group denotation (e.g., 'SWM-Speed' – modified speed data points for Senior walking children).
DOI: 10.5281/zenodo.7377045
title: Data for: Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour

description: These datasets contain supplementary material for the article " Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour " accepted to Fire Safety Journal on April 16, 2023 (DOI 10.1016/j.firesaf.2023.103797). The article presents experimental data sets on the evacuation movement and behaviour of preschool children observed during 15 evacuation drills in 10 nursery schools in the Czech Republic involving 970 children (3-7 years of age) and 87 staff members. In the presented spreadsheets, raw experimental data on speed-density and flow-density relationships are provided separately for corridors, straight staircases (flights, landings, and entire staircase), and doorways. The movement travel speed ('Speed') is expressed in [m·s<sup>-1</sup>], specific flow ('Flow') in [pers·s<sup>-1</sup>·m<sup>-1</sup>], density variable is expressed in the units of [pers·m<sup>−2</sup>] ('Density1') and [m<sup>−2</sup>·m<sup>−2</sup>] ('Density2'). Observations made for the different age groups of children are distinguished by letters: 'J' – Junior, 'S' – Senior, 'S+' - Senior+, 'M' – Mixed. In speed-density data sets, observations for walking children are denoted as 'W', for running children as 'R' (e.g., 'JW' – Junior walking). Speed-density data points that were calculated by excluding waiting times of children (only in the spreadsheets for corridors and landings of straight staircases) are marked 'M' after age group denotation (e.g., 'SWM-Speed' – modified speed data points for Senior walking children).
DOI: 10.5519/qd.smn5q6mq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7000 records)
DOI: 10.34740/kaggle/dsv/5724825
title: TF2 Weapons
DOI: 10.48324/dandi.000548/0.230519.1825
title: Effect of the number of pulses on electroporation by unipolar and 50 % bipolar pulses

description: Trains of 5, 10, or 15 pulses all at 1 Hz. Cells were electroporated by a train of 5, 10, or 15 unipolar 600 ns pulses (0 percent) or bipolar 600 + 600 ns pulses (50 percent) at 1 Hz. 50% represents the second pulse's amplitude in relation to the first pulse's amplitude. 0% represents a unipolar pulse, where no second pulse was applied. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258
DOI: 10.13140/rg.2.2.22196.42884
title: (Rcn), The construction of a good life for all: - Hierarchy of needs and consumption
DOI: 10.5517/ccdc.csd.cc28c0xl
title: CCDC 2095969: Experimental Crystal Structure Determination

description: Related Article: Mehdi Bouchouit, Hadjer Belahlou, Mounia Guergouri, Rafik Bensegueni, Sofiane Bouacida, El-Eulmi Bendeif, Karim Bouchouit, Abdelmalek Bouraiou|2023|J.Mol.Struct.|1287|135674|doi:10.1016/j.molstruc.2023.135674
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28c0ym
title: CCDC 2095970: Experimental Crystal Structure Determination

description: Related Article: Mehdi Bouchouit, Hadjer Belahlou, Mounia Guergouri, Rafik Bensegueni, Sofiane Bouacida, El-Eulmi Bendeif, Karim Bouchouit, Abdelmalek Bouraiou|2023|J.Mol.Struct.|1287|135674|doi:10.1016/j.molstruc.2023.135674
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjfcq
title: CCDC 2190533: Experimental Crystal Structure Determination

description: Related Article: K. Elumalai, K. Sakthi Murugesan|2017|Int.J.ChemTech.Res.|10|170|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjfbp
title: CCDC 2190532: Experimental Crystal Structure Determination

description: Related Article: Periyasamy Murugan|2015|Int.J.ChemTech.Res.|8|128|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjh1g
title: CCDC 2190585: Experimental Crystal Structure Determination

description: Related Article: Ming-Xiong Tan, Yun-Zhi Tang, Yan-Cheng Liu, Zhen-Feng Chen, Hong Liang|2009|J.Indian Chem.Soc.|86|1267|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.1170442
title: Weecology/Portalpredictions 2018-02-09

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1174284
title: Weecology/Portalpredictions 2018-02-16

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1183646
title: Weecology/Portalpredictions 2018-02-23

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1187142
title: Weecology/Portalpredictions 2018-03-02

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1194855
title: Weecology/Portalpredictions 2018-03-09

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1201479
title: Weecology/Portalpredictions 2018-03-16

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1206182
title: Weecology/Portalpredictions 2018-03-23

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1210239
title: Weecology/Portalpredictions 2018-03-30

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1214181
title: Weecology/Portalpredictions 2018-04-06

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1218031
title: Weecology/Portalpredictions 2018-04-13

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1233622
title: Weecology/Portalpredictions 2018-04-27

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1241154
title: Weecology/Portalpredictions 2018-05-04

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1245741
title: Weecology/Portalpredictions 2018-05-11

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1249655
title: Weecology/Portalpredictions 2018-05-18

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1253216
title: Weecology/Portalpredictions 2018-05-25

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1257448
title: Weecology/Portalpredictions 2018-06-01

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1285890
title: Weecology/Portalpredictions 2018-06-08

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1290821
title: Weecology/Portalpredictions 2018-06-15

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1296345
title: Weecology/Portalpredictions 2018-06-22

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1301180
title: Weecology/Portalpredictions 2018-06-29

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1306886
title: Weecology/Portalpredictions 2018-07-06

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1311832
title: Weecology/Portalpredictions 2018-07-13

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1318231
title: Weecology/Portalpredictions 2018-07-20

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1322739
title: Weecology/Portalpredictions 2018-07-27

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1328217
title: Weecology/Portalpredictions 2018-08-03

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1343980
title: Weecology/Portalpredictions 2018-08-12

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1403165
title: Weecology/Portalpredictions 2018-08-24

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1407138
title: Weecology/Portalpredictions 2018-08-31

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1411485
title: Weecology/Portalpredictions 2018-09-07

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1438452
title: Weecology/Portalpredictions 2018-09-28

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1447140
title: Weecology/Portalpredictions 2018-10-05

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1467255
title: Weecology/Portalpredictions 2018-10-19

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1472380
title: Weecology/Portalpredictions 2018-10-26

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1477031
title: Weecology/Portalpredictions 2018-11-02

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1481947
title: Weecology/Portalpredictions 2018-11-09

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1490039
title: Weecology/Portalpredictions 2018-11-16

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1495186
title: weecology/portalPredictions 2018-11-23

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.1747392
title: weecology/portalPredictions 2018-11-30

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2028017
title: weecology/portalPredictions 2018-12-07

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2278220
title: weecology/portalPredictions 2018-12-14

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2485924
title: weecology/portalPredictions 2018-12-21

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2529108
title: weecology/portalPredictions 2018-12-31

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2536494
title: weecology/portalPredictions 2019-01-09

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2543733
title: weecology/portalPredictions 2019-01-18

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2549680
title: weecology/portalPredictions 2019-01-25

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2555168
title: weecology/portalPredictions 2019-02-01

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2560516
title: weecology/portalPredictions 2019-02-08

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2566483
title: weecology/portalPredictions 2019-02-15

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2575824
title: weecology/portalPredictions 2019-02-22

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2581421
title: weecology/portalPredictions 2019-03-01

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2587944
title: weecology/portalPredictions 2019-03-08

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2595245
title: weecology/portalPredictions 2019-03-15

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2616684
title: weecology/portalPredictions 2019-03-29

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2630639
title: weecology/portalPredictions 2019-04-05

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2638136
title: weecology/portalPredictions 2019-04-12

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2647553
title: weecology/portalPredictions 2019-04-19

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2652387
title: weecology/portalPredictions 2019-04-26

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2662527
title: weecology/portalPredictions 2019-05-03

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.2715211
title: weecology/portalPredictions 2019-05-10

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3228429
title: weecology/portalPredictions 2019-05-24

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3236422
title: weecology/portalPredictions 2019-05-31

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3241516
title: weecology/portalPredictions 2019-06-07

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3246516
title: weecology/portalPredictions 2019-06-14

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3252091
title: weecology/portalPredictions 2019-06-21

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3261092
title: weecology/portalPredictions 2019-06-28

description: Using various models to forecast abundances at Portal