cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6963323
title: Data from: Vegetation management shapes arthropod and bird communities in an African savanna

description: Habitat heterogeneity is a key driver of the diversity and distribution of species. African savannas are experiencing changes to their vegetation structure causing shifts towards increased woody plant cover, which results in vegetation structure homogenization. Given the impact that increasing woody plant cover has on patterns of animal use, resource managers across Africa are implementing habitat management practices that are intended to reduce woody plant cover. To understand the ecological implications of various habitat management practices on arthropod and bird communities, we leveraged large-scale tree clearing and subsequent mowing in an African savanna to understand how changes to both the herbaceous layer and woody plant cover (i.e., structural heterogeneity) may shape arthropod and bird communities at the local scale. We focused on four replicated treatments: 1) annual summer mow, 2) annual winter mow, 3) &gt;5 years since last mow (rest), and 4) an adjacent unmanipulated savanna to act as a control. We found that the mowing treatments significantly influenced vegetation structure both with respect to tree density and herbaceous layer. Both arthropod and bird community composition varied across treatments. Grass biomass was the best predictor of arthropod richness and abundance, with arthropods selecting for areas with high biomass. Insectivorous bird richness and abundance was driven by tree density (i.e., perching locations) and not arthropod abundance. Our results suggest that vegetation management practices contribute to habitat heterogeneity at the landscape scale and increase bird species richness through species turnover. However, we caution that if a single vegetation management practice dominates the landscape, it is plausible that it could lead to the simplification of the avian community.
DOI: 10.5281/zenodo.7655207
title: Data from: Vegetation management shapes arthropod and bird communities in an African savanna

description: Habitat heterogeneity is a key driver of the diversity and distribution of species. African savannas are experiencing changes to their vegetation structure causing shifts towards increased woody plant cover, which results in vegetation structure homogenization. Given the impact that increasing woody plant cover has on patterns of animal use, resource managers across Africa are implementing habitat management practices that are intended to reduce woody plant cover. To understand the ecological implications of various habitat management practices on arthropod and bird communities, we leveraged large-scale tree clearing and subsequent mowing in an African savanna to understand how changes to both the herbaceous layer and woody plant cover (i.e., structural heterogeneity) may shape arthropod and bird communities at the local scale. We focused on four replicated treatments: 1) annual summer mow, 2) annual winter mow, 3) &gt;5 years since last mow (rest), and 4) an adjacent unmanipulated savanna to act as a control. We found that the mowing treatments significantly influenced vegetation structure both with respect to tree density and herbaceous layer. Both arthropod and bird community composition varied across treatments. Grass biomass was the best predictor of arthropod richness and abundance, with arthropods selecting for areas with high biomass. Insectivorous bird richness and abundance was driven by tree density (i.e., perching locations) and not arthropod abundance. Our results suggest that vegetation management practices contribute to habitat heterogeneity at the landscape scale and increase bird species richness through species turnover. However, we caution that if a single vegetation management practice dominates the landscape, it is plausible that it could lead to the simplification of the avian community.
DOI: 10.6084/m9.figshare.22123070
title: heart.H3K4me3.pooled_x_SRR9662087_1.fastq.nodup.pval.signal.bigwig

description: The adult zebrafish signal file.
DOI: 10.6084/m9.figshare.22123070.v2
title: heart.H3K4me3.pooled_x_SRR9662087_1.fastq.nodup.pval.signal.bigwig

description: The adult zebrafish signal file.
DOI: 10.6084/m9.figshare.22123070.v1
title: heart.H3K4me3.pooled_x_SRR9662087_1.fastq.nodup.pval.signal.bigwig

description: The adult zebrafish signal file.
DOI: 10.25919/h7sm-wf10
title: RV Investigator Voyage IN2019_T02 End of Voyage (EOV) Archive

description: This record describes the End of Voyage (EOV) archive from the Marine National Facility (MNF) RV Investigator research voyage IN2019_T02, titled "Deep seascapes of the Great Barrier Reef: Uncovering submarine canyons and landslides." The voyage took place between October 4 and October 14, 2019 departing from Brisbane (QLD) and arriving in Darwin (NT). For further information please refer to the voyage documentation links below. Instruments used and data collected include: Regular measurements: Acoustic Doppler Current Profiler (ADCP; 75, 150 KHz ), Lowered ADCP (LADCP), Disdrometer, Fisheries echosounder (EK60), Multibeam Echosounder (EM710, EM122), Sub-bottom Profiler (SBP120), Gravimeter, GPS Positioning System, Doppler Velocity Log, Atmospheric Temperature, Humidity, Pressure, Wind and Rain sensors, Photosynthetically Active Radiation (PAR) sensor, Precision Infrared Radiometer (PIR), Precision Spectral Pyranometer (PSP), Nephelometer, pCO2, Condensation Particle Counters (CPC), Cloud Condensation Nuclei counter (CCN), Multiangle Absorption Photometer (MAAP), Radon, Ozone and Oxygen sensors, Weather Radar, Greenhouse Gas Analysers (Picarro), Infrared Sea Surface Temperature Autonomous Radiometer (ISAR), Fluorometer, Oxygen optode, Thermosalinographs (TSG), CTD, Hydrochemistry. Voyage-specific measurements: Black Carbon Aethalometer, Continuous Plankton Recorder (CPR), Ultra Short BaseLine Underwater Positioning System (USBL), Video, Sonic Anemometer, AIRBOX Radon, Mercury Analyser (Tekran), Gas Chromatograph - Electron Capture Detector (uDirac), Greenhouse Gas Analyser (Spectronus), Cloud Radar (BASTA), Cloud and Aerosol Backscatter Lidar (RMAN), Micro Rain Radar (MRR), Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS), Seastate Cameras, mini Micro-Pulse LIDAR (miniMPL), Chemical Ionisation Mass Spectrometer (CIMS), Neutral Cluster Air Ion Spectrometer (NAIS), Volatility-Hygroscopicity Tandem Differential Mobility Analyser (VH-TDMA), Deep Towed Camera (DTC), Wildlife observations (seabirds, cetaceans and seals). The archive for the IN2019_T02 EOV raw data is curated by the CSIRO Oceans and Atmosphere (O&A) Information and Data Centre (IDC) in Hobart, with a permanent archive at the CSIRO Data Access Portal (DAP, https://data.csiro.au/dap/), providing access to participants and processors of the data collected in the voyage. All voyage documentation is available electronically to MNF support via the local network. Access to voyage documentation for non-CSIRO participants can be made via DataLibrariansOAMNF@csiro.au.
DOI: 10.34740/kaggle/dsv/5028521
title: Contribution Graph (Binary Classification)
DOI: 10.6084/m9.figshare.21823455.v20
title: The ReptileEvolution.com Project

description: Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.5281/zenodo.7425710
title: Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)

description: <strong>Author Information</strong><br> - Diego S. Cardoso, Department of Agricultural Economics - Purdue University<br> - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future <strong>Corresponding Author:</strong> Casey J. Wichman: wichman@gatech.edu <strong>Period of data collection:</strong> 2017--2018 <strong>License: </strong>This data set can be used under terms of the Creative Commons Attribution-ShareAlike 4.0 International license. <strong>Recommended citation for the research paper</strong> <br> Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206 <strong>Recommended citation for the data:</strong> <br> Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563 Please see the README.txt file for the data dictionary and additional information.
description: {"references": ["Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206"]}
DOI: 10.5281/zenodo.6991562
title: Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)

description: <strong>Author Information</strong><br> - Diego S. Cardoso, Department of Agricultural Economics - Purdue University<br> - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future <strong>Corresponding Author:</strong> Casey J. Wichman: wichman@gatech.edu <strong>Period of data collection:</strong> 2017--2018 <strong>License: </strong>This data set can be used under terms of the Creative Commons Attribution-ShareAlike 4.0 International license. <strong>Recommended citation for the research paper</strong> <br> Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206 <strong>Recommended citation for the data:</strong> <br> Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563 Please see the README.txt file for the data dictionary and additional information.
description: {"references": ["Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206"]}
DOI: 10.5281/zenodo.7655269
title: Phylogeny and classification of Endromidae (Lepidoptera: Bombycoidea) based on mitochondrial genomes

description: Incomplete mitochondrial genomes of the study "Phylogeny and classification of Endromidae (Lepidoptera: Bombycoidea) based on mitochondrial genomes".
DOI: 10.5281/zenodo.7655268
title: Phylogeny and classification of Endromidae (Lepidoptera: Bombycoidea) based on mitochondrial genomes

description: Incomplete mitochondrial genomes of the study "Phylogeny and classification of Endromidae (Lepidoptera: Bombycoidea) based on mitochondrial genomes".
DOI: 10.26186/147484
title: Williams Ridge 100 m Bathymetry grid compilation (20220005C)

description: This dataset contains a bathymetry (depth) grid of the Williams Ridge region, southeast Indian Ocean, at 100 metre resolution produced from the compilation of all available source data. These data include single beam echo sounder bathymetry data supplied by the Australian Hydrographic Office to generate the general depth model, and deep-water multibeam bathymetry data to reveal the complexity of the seafloor on Williams Ridge, Kerguelen Plateau and the surrounding abyssal plains and basins. Multibeam bathymetry data were collected during systematic surveys over Williams Ridge by Research Vessel (RV) Investigator in 2020 and RV Sonne, and on vessel transits that crossed through the region. The RV Investigator survey also collected seismic, magnetic and gravity data, and rock samples to provide new knowledge of the geological and tectonic evolution of the region (see www.mnf.csiro.au/en/voyages/IN2020_V01). Austral Fisheries also provided crowdsourced bathymetry (CSB) data from fishing vessels collected using single beam echo sounders. These fishing vessels operate within the Kerguelen Plateau and Williams Ridge region licenced under the Conservation of Antarctic Marine Living Resources. Austral Fisheries CSB data were provided to the Australian Antarctic Division for restricted use in this compilation product. All source multibeam and single beam bathymetry data were edited as 3D point clouds to remove anomalous noise, and given a consistent WGS84 horizontal datum, and where possible, an approximate MSL vertical datum prior to the grid interpolation process. This dataset was developed to support the management of Australia’s marine jurisdiction and is published with permission of the CEO, Geoscience Australia. This dataset is not to be used for navigational purposes
DOI: 10.34740/kaggle/dsv/5028676
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.6084/m9.figshare.22114682
title: NSCLC_Final_dataset

description: Final scRNA-seq dataset of NSCLC tumors from human patients.
DOI: 10.34740/kaggle/dsv/5028699
title: OntoUML/UFO Catalog
DOI: 10.5281/zenodo.7655292
title: Sample, test, and validation data for findmycells

description: findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells <strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong> For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here: Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gtht9d The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7) Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>. The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".
DOI: 10.5281/zenodo.7655291
title: Sample, test, and validation data for findmycells

description: findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells <strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong> For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here: Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gtht9d The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7) Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>. The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".
DOI: 10.26060/ve36-q773
title: Smap Predicted Carbon August 2020 (deprecated)
DOI: 10.6084/m9.figshare.22057106
title: ECOPICS_Mx Plots and map

description: The weather monitoring network that was integrated was composed of two types of equipment: The ONSET HOBO MX2301 Thermohygrometers, with capacity to record temperature and relative humidity data and, to monitor other weather variables of Interest, the Davis Vantage PRO 2 plus weather stations, with recording capabilities fortemperature, precipitation, wind speed and direction, solar radiation, and barometric pressure. The installation of this equipment was carried out at the top of the altitudinal gradient established by the project, within the plots, distributed in altitudinal bands: 3400-3600: 5 Thermohygrometers 3600-3800: 5 Thermohygrometers 3800-4000: 5 Thermohygrometers 4000-4200: 2 Thermohygrometers 4200-4000: 1 Thermohygrometer 4400-4600: 1 Thermohygrometer <br> The meteorological stations were installed at the upper extremes of the altitudinal bands.
DOI: 10.6084/m9.figshare.22057106.v4
title: ECOPICS_Mx Plots and map

description: The weather monitoring network that was integrated was composed of two types of equipment: The ONSET HOBO MX2301 Thermohygrometers, with capacity to record temperature and relative humidity data and, to monitor other weather variables of Interest, the Davis Vantage PRO 2 plus weather stations, with recording capabilities fortemperature, precipitation, wind speed and direction, solar radiation, and barometric pressure. The installation of this equipment was carried out at the top of the altitudinal gradient established by the project, within the plots, distributed in altitudinal bands: 3400-3600: 5 Thermohygrometers 3600-3800: 5 Thermohygrometers 3800-4000: 5 Thermohygrometers 4000-4200: 2 Thermohygrometers 4200-4000: 1 Thermohygrometer 4400-4600: 1 Thermohygrometer <br> The meteorological stations were installed at the upper extremes of the altitudinal bands.
DOI: 10.6084/m9.figshare.22057106.v3
title: ECOPICS_Mx Plots and map

description: The weather monitoring network that was integrated was composed of two types of equipment: The ONSET HOBO MX2301 Thermohygrometers, with capacity to record temperature and relative humidity data and, to monitor other weather variables of Interest, the Davis Vantage PRO 2 plus weather stations, with recording capabilities fortemperature, precipitation, wind speed and direction, solar radiation, and barometric pressure. The installation of this equipment was carried out at the top of the altitudinal gradient established by the project, within the plots, distributed in altitudinal bands: 3400-3600: 5 Thermohygrometers 3600-3800: 5 Thermohygrometers 3800-4000: 5 Thermohygrometers 4000-4200: 2 Thermohygrometers 4200-4000: 1 Thermohygrometer 4400-4600: 1 Thermohygrometer <br> The meteorological stations were installed at the upper extremes of the altitudinal bands.
DOI: 10.34740/kaggle/dsv/5028765
title: Dota 2 Pro League Matches 2023
DOI: 10.6084/m9.figshare.21799793.v8
title: Precision Oncology-Based Targeting of Signaling Pathways Toward a Holistic Approach to Cancer Therapeutics

description: The mutational changes occurring in the regulatory genes responsible for the maintenance of optimal cell growth and proliferation gradually lead to cancer progression and metastasis. Furthermore, patients with the same cancer types often respond differently to different cancer therapies, indicating the need for a patient-specific treatment regimen for the disease. Precision oncology is the new field of cancer research that relies on genetic profiling of individual tumors to identify targetable alterations for custom-tailored personalized treatment of the disease. It is to rely upon the genomic study of cancer cells to get a clear picture of the prognosis and pathways involved in disease progression and to look for the means to selectively target them for a cure. This article intends to briefly explain the foundations and frontiers of precision oncology in the context of technological advances being made in this direction to assess its scope and importance in the realization of a proper cure for cancer.
DOI: 10.6084/m9.figshare.21314016.v1
title: Comparing the isotopic and molecular composition of dissolved organic carbon between the oligotrophic South China Sea and the north Pacific Ocean: signals of biodegradation, conservative mixing, and terrestrial input

description: This data set contains: 1)the sampling information and DOC concentrations of the samples; 2) statistics of DOC concentrations, SPE-DOC concentrations, SPE-DOC Δ14C values, SPE-DOC δ13C values, and carbon extraction efficiencies determined for samples from stations F2 and DC6; 3)Molecular characteristics of DOM throughout two water columns at stations F2 and DC6, including the relative abundance of different compound groups and intensity-weighted indices.
DOI: 10.5281/zenodo.7652965
title: Increased impact of heat domes on 2021-like heat extremes in North America under global warming

description: The key codes and processed data for the paper.
DOI: 10.5281/zenodo.7647837
title: Increased impact of heat domes on 2021-like heat extremes in North America under global warming

description: The key codes and processed data for the paper.
DOI: 10.6084/m9.figshare.22124222
title: Validation of Flipped English Learning Readiness Questionnaire for Persian Students

description: This is the data spss file
DOI: 10.6084/m9.figshare.22124240
title: Anelloviridae contigs

description: Small (&lt;1kb) contigs annotated as belonging to the family Anelloviridae assembled from bladder tumor whole genome sequencing.
DOI: 10.6084/m9.figshare.22124240.v1
title: Anelloviridae contigs

description: Small (&lt;1kb) contigs annotated as belonging to the family Anelloviridae assembled from bladder tumor whole genome sequencing.
DOI: 10.5281/zenodo.7655352
title: Insect COI reference database

description: Insect and arachnid COI reference database, trimmed to the regions amplified by the primers BF1-BR1 This repository includes a fasta file, a trained IDTAXA database, and a PHMM model.
DOI: 10.5281/zenodo.7655351
title: Insect COI reference database

description: Insect and arachnid COI reference database, trimmed to the regions amplified by the primers BF1-BR1 This repository includes a fasta file, a trained IDTAXA database, and a PHMM model.
DOI: 10.5519/qd.j4inkfs7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (67 records)
DOI: 10.25582/data.2020-05.1990057/1926847
title: Data for EMSL Project 50397 from May 2020
DOI: 10.25582/data.2020-05.1989780/1926848
title: Data for EMSL Project 50397 from May 2020
DOI: 10.25582/data.2020-06.2001900/1926849
title: Data for EMSL Project 50397 from June 2020
DOI: 10.25582/data.2020-05.1986054/1926851
title: Data for EMSL Project 50397 from May 2020
DOI: 10.25582/data.2020-05.1988804/1926859
title: Data for EMSL Project 50397 from May 2020