cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/w5665d
title: THE FURCULA IN SUCHOMIMUS TENERENSIS AND TYRANNOSAURUS REX (DINOSAURIA: THEROPODA: TETANURAE)

description: This dataset contains the digitized treatments in Plazi based on the original journal article CHRISTINE LIPKIN, PAUL C. SERENO, JOHN R. HORNER (2007): THE FURCULA IN SUCHOMIMUS TENERENSIS AND TYRANNOSAURUS REX (DINOSAURIA: THEROPODA: TETANURAE). Journal of Paleontology 81, No. 6: 1523-1527, DOI: 10.1666/06-024.1
DOI: 10.25387/g3.11987283
title: Supplemental Material for Félix et al., 2020

description: Table S1 - Sample library information.<br>Table S2 - Gene counts.Table S3 - Full gene list and DEG gene list determined for the KT-treated group and using the V-treated group as a reference for the 30 min time point.Table S4 - Full gene list and DEG gene list determined for the KT-treated group and using the V-treated group as a reference for the 60 min time point.Table S5 - Full gene list and DEG gene list determined for the T-treated group and using the V-treated group as a reference for the 30 min time point.Table S6 - Full gene list and DEG gene list determined for the T-treated group and using the V-treated group as a reference for the 60 min time point.Table S7 - List of DEG genes which overlap between the KT-group and T-group, for the 30 min time point. Table S8 - GO term enrichment analysis. Table S9 - Orthology analysis.File S1 - Supplementary figures (Figures S1 to S4)File S2 - Supplementary material - Comparison of DEG elicited by a transient androgen increase and a social challenge (includes Table S10)
DOI: 10.25387/g3.11987283.v1
title: Supplemental Material for Félix et al., 2020

description: Table S1 - Sample library information.<br>Table S2 - Gene counts.Table S3 - Full gene list and DEG gene list determined for the KT-treated group and using the V-treated group as a reference for the 30 min time point.Table S4 - Full gene list and DEG gene list determined for the KT-treated group and using the V-treated group as a reference for the 60 min time point.Table S5 - Full gene list and DEG gene list determined for the T-treated group and using the V-treated group as a reference for the 30 min time point.Table S6 - Full gene list and DEG gene list determined for the T-treated group and using the V-treated group as a reference for the 60 min time point.Table S7 - List of DEG genes which overlap between the KT-group and T-group, for the 30 min time point. Table S8 - GO term enrichment analysis. Table S9 - Orthology analysis.File S1 - Supplementary figures (Figures S1 to S4)File S2 - Supplementary material - Comparison of DEG elicited by a transient androgen increase and a social challenge (includes Table S10)
DOI: 10.6073/pasta/68ab4e6f628909de50409df766e183d7
title: Soil stratigraphic data for the Toolik Lake region, North Slope of Alaska 2016-2019

description: Soil stratification was determined by measuring the vertical thickness of three main strata (surface acrotelm, mid-depth catotelm, and bottom mineral soil) found in organic-rich or peat soils in the Toolik Lake region, North Slope of Alaska. Additional data for each site include dominant vegetation, landscape position, glacial surface, and microtopography.
DOI: 10.7910/dvn/jnmdco/6rtr0i
title: Minority_USA.tab

description: :unav
DOI: 10.7910/dvn/jnmdco/uhtapq
title: Minority_Hungary_p1.tab

description: :unav
DOI: 10.7910/dvn/jnmdco/vmg7hu
title: Minority_Denmark.tab

description: :unav
DOI: 10.25345/c5km5w
title: MassIVE MSV000085270 - PRM analysis of amino acids in the heart of germ-free and conventional mice (Wisconsin collaboration)
title: Parallel reaction monitoring analysis, comparing amino acids in different heart sections between germ-free and conventional mice. C8 chromatography. Positive mode MS.
DOI: 10.7910/dvn/jnmdco/azavho
title: log.html

description: :unav
DOI: 10.7910/dvn/jnmdco/eelv8n
title: Minority_Hungary_p2.tab

description: :unav
DOI: 10.7910/dvn/jnmdco/hoc7s1
title: experiment_Russian.docx

description: :unav
DOI: 10.7910/dvn/jnmdco/ibjygh
title: Minority_India.csv

description: :unav
DOI: 10.7910/dvn/jnmdco/iqeyii
title: minority_all_data.Rdata

description: :unav
DOI: 10.7910/dvn/jnmdco/r1ag6v
title: minority_data.Rdata

description: :unav
DOI: 10.7910/dvn/jnmdco/te5dex
title: Minority_Russia.tab

description: :unav
DOI: 10.7910/dvn/jnmdco/xeiv86
title: import.R

description: :unav
DOI: 10.7910/dvn/jnmdco/xweooz
title: experiment_Hungarian.docx

description: :unav
DOI: 10.5281/zenodo.3736872
title: Photon Stimulated Desorption of MgS as a Potential Source of Sulfur in Mercury's Exosphere

description: Experimental data for x-ray photoelectron spectroscopy (XPS) and resonance enhanced multiphoton ionization (REMPI) characterization of the surface of magnesium sulfide (MgS) exposed to 193 nm UV photons. These data accompany the manuscript "Photon Stimulated Desorption of MgS as a Potential Source of Sulfur in Mercury’s Exosphere" 'submitted in April 2020 to the Journal of Geophysical Research - Planets. The 'Mg-1s' and 'S-2p' data were used to make the XPS image (Figure 1). The 'power' data were used to make the signal intensity vs. power images (Figures 2 and 3). The 'delay' data were used to fit Maxwellian velocity distributions, and the fits are included (Figure 4). The 'MC' data were the output results of the Monte Carlo model used to determined the density of sulfur as a function of altitude above Mercury (Figure 6).
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l.1
title: C59H48Cu2F6I2NP3
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: This dataset contains experimental data from a sample (InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 31P nuclear magnetic resonance spectroscopy (31P NMR) 19F nuclear magnetic resonance spectroscopy (19F NMR) infrared absorption spectroscopy (IR) mass spectrometry (MS) single crystal X-ray diffraction (single-crystal X-ray diffraction)
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for mass spectrometry (MS) MS (FAB, 3-NBA) m/z [%] = 1419 (1) [M+Cu]+, 1103 (2) [Cu3I2LP]+, 913 (6) [Cu2ILP]+, 884 (3) [Cu2IP2]+, 787 (36) [Cu3I2L]+, 724 (25) [CuLP]+, 695 (9) [CuP2]+, 597 (72) [Cu2IL]+, 408 (100) [CuL]+, 378 (24) [CuP]+, 346 (21) [L+H]+, 317 (14) [P+H]+.
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, DMSO-d6) δ [ppm] = 164.5 (s), 162.0 (s), 136.0 (dd, J = 15.8 Hz, J = 8.3 Hz), 133.3 (bs), 130.1 (s), 128.9 (d, J = 27.7 Hz), 128.6 (d, J = 6.8 Hz), 115.9 (dd, J = 21.3 Hz, J = 9.5 Hz), 31.6 (s), 24.3 (s), resonances could not be assigned due to low resolution even at 8192 ns (49C). Impurities: 3% DCM, 2% THF, 1% phosphine oxide of the ligands according to the 31P-NMR.
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000597
title: 19F nuclear magnetic resonance spectroscopy (19F NMR) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 19F nuclear magnetic resonance spectroscopy (19F NMR) 19F NMR (376 MHz, DMSO-d6) δ [ppm] = −114.79 (s, 6F, F(F-Ph)3P).
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for infrared absorption spectroscopy (IR) IR (ATR) 𝜈̃ [cm–1] = 3053 (vw), 2949 (vw), 2860 (vw), 1586 (m), 1493 (m), 1434 (w), 1392 (w), 1300 (vw), 1223 (m), 1158 (m), 1092 (w), 1013 (w), 825 (m), 742 (w), 693 (m), 634 (w), 517 (m), 496 (w), 467 (m), 443 (m), 431 (m).
DOI: 10.5281/zenodo.3736871
title: Photon Stimulated Desorption of MgS as a Potential Source of Sulfur in Mercury's Exosphere

description: Experimental data for x-ray photoelectron spectroscopy (XPS) and resonance enhanced multiphoton ionization (REMPI) characterization of the surface of magnesium sulfide (MgS) exposed to 193 nm UV photons. These data accompany the manuscript "Photon Stimulated Desorption of MgS as a Potential Source of Sulfur in Mercury’s Exosphere" 'submitted in April 2020 to the Journal of Geophysical Research - Planets. The 'Mg-1s' and 'S-2p' data were used to make the XPS image (Figure 1). The 'power' data were used to make the signal intensity vs. power images (Figures 2 and 3). The 'delay' data were used to fit Maxwellian velocity distributions, and the fits are included (Figure 4). The 'MC' data were the output results of the Monte Carlo model used to determined the density of sulfur as a function of altitude above Mercury (Figure 6).
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000159
title: single crystal X-ray diffraction (single-crystal X-ray diffraction) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for single crystal X-ray diffraction (single-crystal X-ray diffraction)
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6) δ [ppm] = 8.64 (bs, 1H, HPyr), 7.80–7.22 (m, 36H), 2.57 (d, 3JHH = 7.5 Hz, 2H, HCH2), 1.91 (sept, 3JHH = 7.4 Hz, 1H, HCH), 1.54–1.43 (m, 4H, HAlkyl), 1.42–1.36 (m, 2H, HAlkyl), 1.04–0.95 (m, 2H, HAlkyl). Impurities: 3% DCM, 2% THF, 1% phosphine oxide of the ligands according to the 31P-NMR.
DOI: 10.14272/arnrbixdbklrjx-uhfffaoysa-l/chmo0000739
title: 31P nuclear magnetic resonance spectroscopy (31P NMR) ()
title: InChI=1S/C23H24NP.2C18H12F3P.2Cu.2HI/c1-3-11-21(12-4-1)25(22-13-5-2-6-14-22)23-18-20(15-16-24-23)17-19-9-7-8-10-19;2*19-13-1-7-16(8-2-13)22(17-9-3-14(20)4-10-17)18-11-5-15(21)6-12-18;;;;/h1-6,11-16,18-19H,7-10,17H2;2*1-12H;;;2*1H/q;;;2*+1;;/p-2

description: dataset for 31P nuclear magnetic resonance spectroscopy (31P NMR) 31P NMR (162 MHz, DMSO-d6) δ [ppm] = −5.09 (bs, 1P, PCyclopentPyrPhos), −12.90 (bs, 2P, P(F-Ph)3P). Impurities: 1% phosphine oxide of the ligands.
DOI: 10.21227/341g-dg90
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, insufficient and private training datasets have impeded the widespread development and dissemination of image classification algorithms for environment recognition. To address these shortcomings, we developed ExoNet - the first open-source large-scale hierarchical dataset of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical classification architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.
DOI: 10.21227/jvh5-wc14
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, insufficient and private training datasets have impeded the widespread development and dissemination of image classification algorithms for environment recognition. To address these shortcomings, we developed ExoNet - the first open-source large-scale hierarchical dataset of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, collected using a lightweight wearable smartphone camera system. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical classification architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.
DOI: 10.5281/zenodo.3747202
title: Supplementary data for Cariou et al (2020, Molecular Ecology Resources, "How consistent is RAD-seq divergence with DNA-barcode based clustering in insects?")

description: This dataset accompanies a paper by Cariou et al, to be published in Molecular Ecology Resources, where we assessed in 92 insect species if the genetic clustering of specimens into species like units, on the basis of mitochondrial DNA, was consistent with genome wide divergence, as estimated by RAD-seq data. The present repository includes: (1) a detailed description of the bioinformatic analysis indicating which programs were used, together with parameter values, (2) the raw RAD-seq data following demultiplexing, (3) the consensus sequences of all RAD loci for all specimens, and (4) large tables indicating genetic distances at all RAD loci for all species.
DOI: 10.5281/zenodo.3747203
title: Supplementary data for Cariou et al (2020, Molecular Ecology Resources, "How consistent is RAD-seq divergence with DNA-barcode based clustering in insects?")

description: This dataset accompanies a paper by Cariou et al, to be published in Molecular Ecology Resources, where we assessed in 92 insect species if the genetic clustering of specimens into species like units, on the basis of mitochondrial DNA, was consistent with genome wide divergence, as estimated by RAD-seq data. The present repository includes: (1) a detailed description of the bioinformatic analysis indicating which programs were used, together with parameter values, (2) the raw RAD-seq data following demultiplexing, (3) the consensus sequences of all RAD loci for all specimens, and (4) large tables indicating genetic distances at all RAD loci for all species.
DOI: 10.6073/pasta/e02e3bde8ba74a045eb907d7faa103fd
title: Circulation dynamics: currents, waves, temperature measurements from moorings in lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing

description: Starting August 2018, five moorings deployed on the seafloor of multiple lagoons in the Beaufort Sea will record currents, waves, temperature, and pressure. Moorings are retrieved and re-deployed each August. This data is being collected to better understand the multi-seasonal circulation dynamics between the Beaufort Sea and coastal lagoons. Two moorings are deployed in Elson Lagoon, one in Stefansson Sound, one in Jago Lagoon, and one in Kaktovik Lagoon. Each mooring contains two data loggers: RBRduo3 T.D wave loggers and Lowell Instruments LLC TCM-1 tilt current meters. The RBR instruments measure temperature, pressure, and derived wave energy, average wave period, average wave height, maximum wave period, maximum wave height, 1/10 wave period, 1/10 wave height, significant wave period, significant wave height, tidal slope, depth, and sea pressure. The Lowell LLC TML-1 tilt current meters measure water velocity, heading, and temperature.
DOI: 10.25345/c5fx30
title: MassIVE MSV000085271 - Posternak_PROTACdegradation_P124_Lumo
title: The files are associated with a manuscript submitted for publication by Ganna Posternak et al. The main goal of this paper was functionally characterize the mechanism of action of a PROTAC designed to target BRAFV600E. This dataset provides a proteome-wide view of protein degradation after treatment with the PROTAC in question.
DOI: 10.18112/openneuro.ds002655.v1.0.1
title: The medial temporal lobe supports mnemonic discrimination for event duration
DOI: 10.5281/zenodo.3747770
title: Project B2 - Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data

description: The purpose of this research is to evaluate work zone mobility using SHRP2 NDS naturalistic driving data. In this study, forward, rear-view video, and time series traces for traversed work zones are collected at 0.1 second intervals. Using the forward and rear-ward videos work zone configurations such as traffic control devices, area types, presence of dynamic message signs (DMS), intelligent transportation systems technologies (ITS), as well as the presence of workers and equipment are identified. These videos are also used for estimating traffic density by observing the number of vehicles surrounding the participant, and average traffic flow by observed speed and traffic density. The capacities of different work zone sections and configurations are estimated based on developed speed-flow relationships and this capacity is used to verify and calibrate the work zone capacity method defined by the newest addition of Highway Capacity Manual (HCM). The probability of breakdown is then estimated for each level of flow rates and volume to capacity ratios can be used to verify the results of HCM.
DOI: 10.5281/zenodo.3747771
title: Project B2 - Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data

description: The purpose of this research is to evaluate work zone mobility using SHRP2 NDS naturalistic driving data. In this study, forward, rear-view video, and time series traces for traversed work zones are collected at 0.1 second intervals. Using the forward and rear-ward videos work zone configurations such as traffic control devices, area types, presence of dynamic message signs (DMS), intelligent transportation systems technologies (ITS), as well as the presence of workers and equipment are identified. These videos are also used for estimating traffic density by observing the number of vehicles surrounding the participant, and average traffic flow by observed speed and traffic density. The capacities of different work zone sections and configurations are estimated based on developed speed-flow relationships and this capacity is used to verify and calibrate the work zone capacity method defined by the newest addition of Highway Capacity Manual (HCM). The probability of breakdown is then estimated for each level of flow rates and volume to capacity ratios can be used to verify the results of HCM.
DOI: 10.5281/zenodo.3747901
title: Web Scrap of main informations on COVID 19 macro data per country

description: We've gathered a single point of the worldwide evolution of daily cases of population affected by COVID 19 crises. Information is web-scrapped twice a day on the below fields. <strong>dateTime</strong> type STRING "Fecha de la captura del datos <strong>country</strong> type STRING "País al que hacen referencia los datos, salvo un crucero" <strong>total_cases</strong> type INT "Número total de casos que se han contabilizado en el país" <strong>new_cases</strong> type INT "Número total de casos incrementados desde la info anterior en el país" <strong>total_deaths</strong> type INT "Número total de fallidos en el país " <strong>new_deathts</strong> type INT "Número total de fallecidos incrementados desde la info anterior en el país" <strong>total_recovered </strong>type INT "Número total dados de alta en el país" <strong>active_cases </strong>type INT "Casos que están recibiendo tratamiento en el país" <strong>critical_cases </strong>type INT "Número total de casos en la UCI en el país" <strong>cases_per_million </strong>type INT "Número total de casos por millón de habitantes en el país" <strong>deaths_per_million </strong>type INT "Número total de fallecidos por millón de habitantes en el país"
description: UTF-8, Semicolon sep,
DOI: 10.5281/zenodo.3747902
title: Web Scrap of main informations on COVID 19 macro data per country

description: We've gathered a single point of the worldwide evolution of daily cases of population affected by COVID 19 crises. Information is web-scrapped twice a day on the below fields. <strong>dateTime</strong> type STRING "Fecha de la captura del datos <strong>country</strong> type STRING "País al que hacen referencia los datos, salvo un crucero" <strong>total_cases</strong> type INT "Número total de casos que se han contabilizado en el país" <strong>new_cases</strong> type INT "Número total de casos incrementados desde la info anterior en el país" <strong>total_deaths</strong> type INT "Número total de fallidos en el país " <strong>new_deathts</strong> type INT "Número total de fallecidos incrementados desde la info anterior en el país" <strong>total_recovered </strong>type INT "Número total dados de alta en el país" <strong>active_cases </strong>type INT "Casos que están recibiendo tratamiento en el país" <strong>critical_cases </strong>type INT "Número total de casos en la UCI en el país" <strong>cases_per_million </strong>type INT "Número total de casos por millón de habitantes en el país" <strong>deaths_per_million </strong>type INT "Número total de fallecidos por millón de habitantes en el país"
description: UTF-8, Semicolon sep,
DOI: 10.5519/qd.djo3zq30
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5066/p9ll6yor
title: Lake Erie Fish Community Data, 2013-2019

description: Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based fishery management, and supports decision making that considers food-web interactions. In support of binational Great Lakes fishery management the objectives of this survey were to: provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes in tandem with water quality information, and to assess year class strength. A systematic grid sampling approach with 41 stations was sampled via bottom trawl during June (Spring) and September (Autumn), starting in 2013. This data release adds 2019 data to the set for a total of seven years observation using the same gear and sampling design.