cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25914/gbvq-1d18
title: Himawari 8/9 Region 03 Himawari Standard Data - Archive (ARC) data stream
DOI: 10.25914/4nz6-jf31
title: Himawari 8/9 Full Disk Himawari Standard Data - Archive (ARC) data stream
DOI: 10.25914/47hz-cv91
title: Himawari 8/9 Full Disk Himawari Standard Data - Near Real-Time (NRT) data stream
DOI: 10.25914/ppg0-pv57
title: Himawari 8/9 Region 03 Himawari Standard Data - Near Real-Time (NRT) data stream
DOI: 10.5519/qd.fvba9zpe
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3640 records)
DOI: 10.5281/zenodo.7681900
title: Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"

description: This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior
DOI: 10.5281/zenodo.7681899
title: Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"

description: This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior
DOI: 10.5281/zenodo.7683084
title: Extraction of Crossref datadump for benchmarking purpose (minify version)

description: A chunk of Crossref datadump minified (removing whitespace)
DOI: 10.5281/zenodo.7683083
title: Extraction of Crossref datadump for benchmarking purpose (minify version)

description: A chunk of Crossref datadump minified (removing whitespace)
DOI: 10.6073/pasta/45eb220226f5557a11d2fb12eef0c8da
title: Dataset from a study of aquatic macrophyte phenology across three sites in the Coeur d'Alene Lake Basin.

description: This repository includes data on aquatic macrophytes, water temperature, water clarity, and other water quality measures, such as pH, dissolved oxygen, and specific conductance relative to understanding the growth cycle (i.e., phenology) of aquatic plants. The data was generated during a project to study the phenology of macrophytes across the growing season of 2017, in three temperate lakes of the Coeur d’Alene Basin, USA. The project’s aim was to demarcate the timing of initial macrophyte growth, peak biomass, and senescence. The project also sought to compare traditional biomass measures with sonar biovolume (percent water column occupied by vegetation), which is relatively easier to collect. Macrophytes were measured twice per month with biomass collection via rake twirl, sonar biovolume, and species identification. Hourly water temperatures were measured over the course of the growing season at each site in shallow water near the macrophyte collection sites. The hourly water temperature data was used to calculate cumulative growing degree days over the growing season using minimum and maximum growth thresholds for Myriophyllum spicatum. Water temperature was also measured with a multi-parameter sonde at 0.5 meter increments from the water surface to the lake sediment and collected twice per month. Photosynthetically active radiation was measured during these vertical profiles and light extinction was calculated from this data. Water clarity was also measured with a standard black and white 20 centimeter Secchi disk. The data in this repository was originally reported in the following publication: Torso, K., Scofield, B.D. and Chess, D.W., 2020. Variations in aquatic macrophyte phenology across three temperate lakes in the Coeur d’Alene Basin. Aquatic Botany, 162, p.103209. https://doi.org/10.1016/j.aquabot.2020.103209
DOI: 10.34740/kaggle/dsv/5077340
title: US counties COVID 19 dataset
DOI: 10.6073/pasta/e0f653ab1ba1163e1fd040afe57d4159
title: Mollusc population abundance monitoring: Fall 2016 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10

description: This data set is the Fall 2016 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-1707a.
DOI: 10.6073/pasta/5df6ef1e2c10d73447f51bf6f6f989b1
title: Mollusc population size distribution monitoring: Fall 2016 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10

description: This data set is the Fall 2016 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1707. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
DOI: 10.6073/pasta/e90f9eef7e24b0dd75df4d04932fe977
title: Long-term monitoring of seven high marsh plant mixtures on Sapelo Island GA

description: We set up plots (1x2m) in 1996 in mixtures or on vegetation borders at three sites. We set up 16 plots for each of seven mixtures - Marsh Landing (Juncus-Meadow, Spartina-Meadow, and Spartina-Batis mixtures), Teal Boardwalk (Juncus-Spartina mixture) and Shell Hammock (Juncus-Spartina, Juncus-Meadow, and Spartina-Meadow mixtures). Half were watered with fresh water 3 times per week (W) and half were controls (C). Watering stopped in April 2000. Plots were monitored to document changes in plant community composition (percent cover) over time.
DOI: 10.6073/pasta/d3709c4dc304cf55c903a64658c82870
title: Disturbance to monitoring plots at Altamaha River Plant Transition Sites SCSA, ZSC1, and ZSC2 from 2013 to 2022.

description: We established permanent vegetation monitoring plots at Altamaha River plant transition sites in 2012. Starting in 2013, when we recorded plant sizes, we also noted any disturbance to the plots. Plots were scored as normal (no visible disturbance), disturbed by wrack (wrack present in plots and stems dead or broken), disturbed by snails (&gt;100 Littoraria per square meter and plant biomass low), disturbed by pigs (animal trail through the plot), initial slump (plot at the creekbank sliding into the creek based on movement of pvc poles or formation of a crevasse), and terminal slump (plot had slid far enough down that vegetation had drowned). Plots that experienced terminal slump or could not be found for any reason were scored as lost. Lost plots were replaced with a new plot in the same general area with the plot code incremented by 10. For example if plot 3 was lost, it was replaced by 13, and then in turn by 23.
DOI: 10.5281/zenodo.7683111
title: Resources for Page Stream Segmentation dataset SIGIR 2023

description: Resources for the paper "SIGIR 2023 Short Paper: Agglomerative Clustering for Page Stream Segmentation", submitted to the short track of SIGIR 2023
DOI: 10.5281/zenodo.7683110
title: Resources for Page Stream Segmentation dataset SIGIR 2023

description: Resources for the paper "SIGIR 2023 Short Paper: Agglomerative Clustering for Page Stream Segmentation", submitted to the short track of SIGIR 2023
DOI: 10.5281/zenodo.7343311
title: Extraction of Crossref datadump for benchmarking purpose (small version)

description: Several aggregation of Crossref datadump to amplify the size for benchmarking purpose.
DOI: 10.5281/zenodo.7343312
title: Extraction of Crossref datadump for benchmarking purpose (small version)

description: Several aggregation of Crossref datadump to amplify the size for benchmarking purpose.
DOI: 10.5281/zenodo.7682834
title: Extraction of Crossref datadump for benchmarking purpose (small version)

description: Several aggregation of Crossref datadump to amplify the size for benchmarking purpose.
DOI: 10.5281/zenodo.7683117
title: Extraction of Crossref datadump for benchmarking purpose (small version)

description: Several aggregation of Crossref datadump to amplify the size for benchmarking purpose.
DOI: 10.6069/61bx-2p22
title: Validation of known and novel AKT, S6K and RSK targets by targeted MS

description: Using the global SILAC and label-free phosphoproteomics experiments as a basis, an inclusion
list for validation of known and novel AKT, RSK and S6K targets comprising the RxRxxp[ST]
motif was generated. Samples were analyzed by PRM-MS.
DOI: 10.6084/m9.figshare.22186330
title: Carbon monoxide data, hospital regions, Jan 2019 to Nov 2022

description: We extracted the carbon monoxide (CO) data, or vertically integrated CO column density (unit, mol/m^2 ; with "CO_column_number_density" as the band variable). And we summarized the TROPOMI extracted CO data in rds format (R data serialization format).
DOI: 10.6084/m9.figshare.22186330.v1
title: Carbon monoxide data, hospital regions, Jan 2019 to Nov 2022

description: We extracted the carbon monoxide (CO) data, or vertically integrated CO column density (unit, mol/m^2 ; with "CO_column_number_density" as the band variable). And we summarized the TROPOMI extracted CO data in rds format (R data serialization format).
DOI: 10.6084/m9.figshare.22186393
title: Formaldehyde data, hospital regions, Jan 2019 to Nov 2022

description: We extracted the formaldehyde (HCHO) data, or Tropospheric HCHO column number density. (unit, mol/m^2 ; with "tropospheric_HCHO_column_number_density " as the band variable) from "COPERNICUS/S5P/OFFL/L3_HCHO" with a user-defined code in Google earth engine system. And we summarized the TROPOMI extracted HCHO data in rds format (R data serialization format). Values were multiplied by 1,000,000 (10^6) to facilitate the extraction process (Final Unit. mol/square millimeter, not mol/square meter) by using the below code "collection = collection.map(function(image) { return image.addBands(image.multiply(1000000).select([0], ['tropospheric_HCHO_column_number_density2'])); });" <br> <br>
DOI: 10.6084/m9.figshare.22186393.v1
title: Formaldehyde data, hospital regions, Jan 2019 to Nov 2022

description: We extracted the formaldehyde (HCHO) data, or Tropospheric HCHO column number density. (unit, mol/m^2 ; with "tropospheric_HCHO_column_number_density " as the band variable) from "COPERNICUS/S5P/OFFL/L3_HCHO" with a user-defined code in Google earth engine system. And we summarized the TROPOMI extracted HCHO data in rds format (R data serialization format). Values were multiplied by 1,000,000 (10^6) to facilitate the extraction process (Final Unit. mol/square millimeter, not mol/square meter) by using the below code "collection = collection.map(function(image) { return image.addBands(image.multiply(1000000).select([0], ['tropospheric_HCHO_column_number_density2'])); });" <br> <br>
DOI: 10.5281/zenodo.7604075
title: Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em> These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong> <br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym
DOI: 10.5281/zenodo.7604074
title: Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em> These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong> <br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym
DOI: 10.15151/esrf-es-1034612119
title: Investigations of precipitation and mechnical properties in an aluminum alloy elaborated by metal additive manufacturing

description: In metal additive manufacturing, the large thermal anisotropy, which takes place during the deposition and the melting of the metallic powder, leads to anisotropic grain microstructures and highly heterogeneous precipitagtion. The knowledge of thermal, physical and metallurgical phenomena developing from the nanometric to the mesoscopic scale is essential to understand and model the behavior of these new class of materials, to develop alloy composition adapted to the thermal and mechanical loadings of components during service. In this study, the Laue microdiffraction technique is used to investigate the crystallography, the chemistry and the strain hardening in an aeronautical aluminum model alloy elaborated by selective laser melting.
DOI: 10.5281/zenodo.6658453
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Andrea Acurio, https://orcid.org/0000-0002-1792-7107. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.6658454
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Andrea Acurio, https://orcid.org/0000-0002-1792-7107. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.6672785
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Andrea Acurio, https://orcid.org/0000-0002-1792-7107. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.7033259
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Andrea Acurio, https://orcid.org/0000-0002-1792-7107. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.7683181
title: Framework of virtual platforms for learning and developing competencies

description: Database used for the SLR of the "Framework of virtual platforms for learning and developing competencies" paper.
DOI: 10.5281/zenodo.7683180
title: Framework of virtual platforms for learning and developing competencies

description: Database used for the SLR of the "Framework of virtual platforms for learning and developing competencies" paper.
DOI: 10.5281/zenodo.7683126
title: Basic helix-loop-helix pioneer factors interact with the histone octamer to invade nucleosomes and generate nucleosome depleted regions.

description: This dataset includes all time traces from single-molecule experiments as well as raw gel images used in the manuscript. All time traces from single-molecule experiments and can be viewed using vbFRET (https://vbfret.sourceforge.net/).
DOI: 10.5281/zenodo.7683125
title: Basic helix-loop-helix pioneer factors interact with the histone octamer to invade nucleosomes and generate nucleosome depleted regions.

description: This dataset includes all time traces from single-molecule experiments as well as raw gel images used in the manuscript. All time traces from single-molecule experiments and can be viewed using vbFRET (https://vbfret.sourceforge.net/).
DOI: 10.5066/p95wct5t
title: Surface-Water-Quality Data and Time-Series Plots to Support Implementation of Site-Dependent Aluminum Criteria in Massachusetts, 2018-19 (ver. 1.1, February 2023)

description: This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0 (U.S. Environmental Protection Agency, 2018). Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis). Reference: U.S. Environmental Protection Agency, 2018, Final aquatic life ambient water quality criteria for aluminum - 2018: U.S. Environmental Protection Agency, document EPA-822-R-18-001, 161 p., apps., accessed March 12, 2020, at https://www.epa.gov/sites/production/files/2018-12/documents/aluminum-final-national-recommended-awqc.pdf.