cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23684121
title: Lists of CNE-associated genes

description: This dataset contains lists of genes ranked by number of associated CNEs. Only genes with more CNEs than average are included. CNE-gene association was determined by finding, for each CNE, the closest gene in the genome. <br> Description of files: The first four letters of each file are the species identifier (SpID). For each species, four types of lists are shown: - SpID_ranked_genes.tsv : all genes are included - SpID_homeo_ranked.tsv : only homeodomain-containing genes are included - SpID_immuno_ranked.tsv : only immunoglobulin-like domain containing genes are included - SpID_TF_ranked.tsv : only transcription factors (excluding homeodomain) identified in protein domain overrepresentation analysis are included.
DOI: 10.5281/zenodo.7886214
title: A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain

description: <strong>Quality-controlled predictions of deep learning models for cell counting</strong> This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: <em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em> The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. <strong>Content</strong> The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. <strong>Folder Structure and file naming conventions</strong> There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).
description: This work was funded by: AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); the Tuscany Health Ecosystem (THE) Project (CUP I53C22000780001), funded by the National Recovery and Resilience Plan (NRPP), within the NextGeneration Europe (NGEU) Program; PRIN2017 2017HMH8FA to T.P., R.M. was supported by Fondazione Umberto Veronesi.
DOI: 10.5281/zenodo.7886215
title: A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain

description: <strong>Quality-controlled predictions of deep learning models for cell counting</strong> This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: <em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em> The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. <strong>Content</strong> The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. <strong>Folder Structure and file naming conventions</strong> There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).
description: This work was funded by: AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); the Tuscany Health Ecosystem (THE) Project (CUP I53C22000780001), funded by the National Recovery and Resilience Plan (NRPP), within the NextGeneration Europe (NGEU) Program; PRIN2017 2017HMH8FA to T.P., R.M. was supported by Fondazione Umberto Veronesi.
DOI: 10.5281/zenodo.7582285
title: Dataset for article titled "Assessing continuum plasticity postulates with grain stress and local strain measurements in triaxially compressed sand"

description: Dataset containing raw and processed data from experiments presented in the article titled "Assessing continuum plasticity postulates with grain-scale stress and strain measurements in triaxially-compressed sand". An enclosed PDF file describes the data.
description: All authors acknowledge support from the U.S. National Science Foundation CAREER Award No. CBET-1942096. Experiments were performed under General User Proposal (GUP)-72658 at the Advanced Photon Source (APS) beamline ID-1. The authors are grateful for this beamtime and assistance from beamline scientists Peter Kenesei and Jun-Sang Park. Use of APS, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357.
DOI: 10.5281/zenodo.7582286
title: Dataset for article titled "Assessing continuum plasticity postulates with grain stress and local strain measurements in triaxially compressed sand"

description: Dataset containing raw and processed data from experiments presented in the article titled "Assessing continuum plasticity postulates with grain-scale stress and strain measurements in triaxially-compressed sand". An enclosed PDF file describes the data.
description: All authors acknowledge support from the U.S. National Science Foundation CAREER Award No. CBET-1942096. Experiments were performed under General User Proposal (GUP)-72658 at the Advanced Photon Source (APS) beamline ID-1. The authors are grateful for this beamtime and assistance from beamline scientists Peter Kenesei and Jun-Sang Park. Use of APS, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357.
DOI: 10.5281/zenodo.8146514
title: Comparison of news commonality and churn in international news outlets with TARO (auxiliary material)

description: Dataset with snapshots of published news in Spiegel, France24, ANSA, ilPost, BBC, ABC (16,17,18,23 of March 2023).<br> Used for the analyses presented in case study 1 and 2. Giuseppe Carrino, Angelo Di Iorio, and Gioele Barabucci. 2023. <strong>Comparison of news commonality and churn in international news outlets with TARO</strong>. In <em>33rd ACM Conference on Hypertext and Social Media (HT ’23), September 4–8, 2023, Rome, Italy. ACM, New York, NY, USA</em>. https://doi.org/10.1145/3603163.3609062
DOI: 10.5281/zenodo.8146513
title: Comparison of news commonality and churn in international news outlets with TARO (auxiliary material)

description: Dataset with snapshots of published news in Spiegel, France24, ANSA, ilPost, BBC, ABC (16,17,18,23 of March 2023).<br> Used for the analyses presented in case study 1 and 2. Giuseppe Carrino, Angelo Di Iorio, and Gioele Barabucci. 2023. <strong>Comparison of news commonality and churn in international news outlets with TARO</strong>. In <em>33rd ACM Conference on Hypertext and Social Media (HT ’23), September 4–8, 2023, Rome, Italy. ACM, New York, NY, USA</em>. https://doi.org/10.1145/3603163.3609062
DOI: 10.5281/zenodo.8146636
title: cMSSM parameter space points generated with SPheno and micrOMEGAS

description: These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in pre-SUSY 2023 summer school in Southampton. The code used to generate and to analyse these data can be found here. The datasets are as following: 1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are 'm0', 'm12', 'A0', 'tanb': the four physical parameters of the theory 'idx': an utility identifier used during generation, can/should be ignored The flattened SPheno outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file. 10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are: The same as above, in addition to 'Omega', 'dm_spin', 'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively. The full list of columns can be seen in `column_names.txt` file. Versions: SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found here. micrOMEGAS 5.3.41, with the MSSM model adapted for low-scale slha inputs. The datasets are provided in Apache `parquet` format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use `pyarrow`.
DOI: 10.5281/zenodo.8146635
title: cMSSM parameter space points generated with SPheno and micrOMEGAS

description: These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in pre-SUSY 2023 summer school in Southampton. The code used to generate and to analyse these data can be found here. The datasets are as following: 1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are 'm0', 'm12', 'A0', 'tanb': the four physical parameters of the theory 'idx': an utility identifier used during generation, can/should be ignored The flattened SPheno outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file. 10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are: The same as above, in addition to 'Omega', 'dm_spin', 'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively. The full list of columns can be seen in `column_names.txt` file. Versions: SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found here. micrOMEGAS 5.3.41, with the MSSM model adapted for low-scale slha inputs. The datasets are provided in Apache `parquet` format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use `pyarrow`.
DOI: 10.5281/zenodo.8147019
title: Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'

description: A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255
description: {"references": ["Lees, J. A., Croucher, N. J., Goldblatt, D., Nosten, F., Parkhill, J., Turner, C., Turner, P., & Bentley, S. D. (2017). Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration. eLife, 6, e26255."]}
DOI: 10.5281/zenodo.8147018
title: Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'

description: A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255
description: {"references": ["Lees, J. A., Croucher, N. J., Goldblatt, D., Nosten, F., Parkhill, J., Turner, C., Turner, P., & Bentley, S. D. (2017). Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration. eLife, 6, e26255."]}
DOI: 10.5281/zenodo.8145105
title: Catalog of Sagittarius Stream Stars with Gaia BP/RP Metallicities

description: This catalog contains 34,240 stars selected as high probability Sagittarius stream members (selected in Vasiliev et al. 2021) with reported metallicities inferred from Gaia BP/RP spectra (measured in Andrae et al. 2023), as well as their Sagittarius/disk/outlier membership probabilities under the model described in Cunningham et al. (2023).
DOI: 10.5281/zenodo.8145104
title: Catalog of Sagittarius Stream Stars with Gaia BP/RP Metallicities

description: This catalog contains 34,240 stars selected as high probability Sagittarius stream members (selected in Vasiliev et al. 2021) with reported metallicities inferred from Gaia BP/RP spectra (measured in Andrae et al. 2023), as well as their Sagittarius/disk/outlier membership probabilities under the model described in Cunningham et al. (2023).
DOI: 10.5281/zenodo.8147032
title: Catalog of Sagittarius Stream Stars with Gaia BP/RP Metallicities

description: This catalog contains 34,240 stars selected as high probability Sagittarius stream members (selected in Vasiliev et al. 2021) with reported metallicities inferred from Gaia BP/RP spectra (measured in Andrae et al. 2023), as well as their Sagittarius/disk/outlier membership probabilities under the model described in Cunningham et al. (2023).
DOI: 10.5284/1110590
title: Finds Hub Data from Boundary objects: Using grave goods to link communities, past and present

description: This collection comprises data from the Finds Hub, a project designed, built and hosted by Exegesis/Idox Ltd, in consultation with the Boundary Objects Project team and partner organisations. The Boundary Objects project, which received AHRC follow-on funding for impact and engagement, aimed to capitalise on a key realisation of the Prehistoric Grave Goods parent project (https://doi.org/10.5284/1052206) - that grave goods hold tremendous powers to captivate and to connect people in the present as well as in prehistory.
DOI: 10.5281/zenodo.7849393
title: Data for "Mixed viral infection constrains the genome formula of multipartite cucumber mosaic virus"

description: We performed a study to assess the effect of mixed infection on the genome formula of multipartite virus CMV. We used qPCR to determine genome formulas and titer. Additionally, simulation models were developed to describe mechanisms for genome formula change under mixed infection.
DOI: 10.5281/zenodo.7849392
title: Data for "Mixed viral infection constrains the genome formula of multipartite cucumber mosaic virus"

description: We performed a study to assess the effect of mixed infection on the genome formula of multipartite virus CMV. We used qPCR to determine genome formulas and titer. Additionally, simulation models were developed to describe mechanisms for genome formula change under mixed infection.
DOI: 10.25403/upresearchdata.23683596.v1
title: Effect of hydrogen peroxide applied as a foliar spray on vegetables

description: The plant height, leaf area index and stomatal density as affected by the foliar application of hydrogen peroxide is given over time. The treatments were applied at two concentrations (HP1 and HP2) either weekly (*) or twice a week (o). The data of the plants that received no treatment (C) are also present.
DOI: 10.25403/upresearchdata.23683596
title: Effect of hydrogen peroxide applied as a foliar spray on vegetables

description: The plant height, leaf area index and stomatal density as affected by the foliar application of hydrogen peroxide is given over time. The treatments were applied at two concentrations (HP1 and HP2) either weekly (*) or twice a week (o). The data of the plants that received no treatment (C) are also present.
DOI: 10.5281/zenodo.8146998
title: A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.

description: Raw Data for PeerJ
DOI: 10.5281/zenodo.8146997
title: A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.

description: Raw Data for PeerJ
DOI: 10.25921/fztr-mf31
title: Regional Hydrodynamic Model Outputs of the NOAA Operational Forecast System (OFS)
DOI: 10.6084/m9.figshare.19221618
title: Habitat shifts and biotic velocity of climate change

description: The dataset is focused on alpine species from the Canary Islands and the data is the result from species distribution models. The article to which this data is related is under revision, so the data maybe subject to change.<br> <br> The dataset is divided in two main variables surface (S_; in hectares) and biotic velocity response to climate change (V_; in m/year) in different scenarios: past: recent past (1959-1989) pres: present scenario (1990-2019) fut85_4160: future scenario RCP 8.5 2041-2060 fut85_61_80: future scenario RCP 8.5 2061-2080 The species are classified by island (TF = Tenerife, LP = La Palma), origin (SIE = single island endemics, CAN = canarian endemics) and threat category (TH = Threatened; NT = Not threatened). <br> Related research: Bello-Rodríguez et al. 2022 (Biodiversity and Conservation) <br> <br>
DOI: 10.6084/m9.figshare.19221618.v3
title: Habitat shifts and biotic velocity of climate change

description: The dataset is focused on alpine species from the Canary Islands and the data is the result from species distribution models. The article to which this data is related is under revision, so the data maybe subject to change.<br> <br> The dataset is divided in two main variables surface (S_; in hectares) and biotic velocity response to climate change (V_; in m/year) in different scenarios: past: recent past (1959-1989) pres: present scenario (1990-2019) fut85_4160: future scenario RCP 8.5 2041-2060 fut85_61_80: future scenario RCP 8.5 2061-2080 The species are classified by island (TF = Tenerife, LP = La Palma), origin (SIE = single island endemics, CAN = canarian endemics) and threat category (TH = Threatened; NT = Not threatened). <br> Related research: Bello-Rodríguez et al. 2022 (Biodiversity and Conservation) <br> <br>
DOI: 10.6084/m9.figshare.19221618.v2
title: Habitat shifts and biotic velocity of climate change

description: The dataset is focused on alpine species from the Canary Islands and the data is the result from species distribution models.<br><br>The dataset is divided in two main variables surface (S_; in hectares) and biotic velocity response to climate change (V_; in m/year) in different scenarios:past: recent past (1959-1989)pres: present scenario (1990-2019)fut85_4160: future scenario RCP 8.5 2041-2060fut85_61_80: future scenario RCP 8.5 2061-2080The species are classified by island (TF = Tenerife, LP = La Palma), origin (SIE = single island endemics, CAN = canarian endemics) and threat category (TH = Threatened; NT = Not threatened).<br>Related research: Bello-Rodríguez et al. 2022 (Biodiversity and Conservation)<br><br>
DOI: 10.6084/m9.figshare.21756230.v2
title: Dataset:

description: Exploration metrics of juvenile newts, Ichthyosaura alpestris and Lissotriton vulgaris, during acute temperature change. Juveniles were exposed to two rearing thermal regimes for five weeks prior to behavioral assays. Repeated trials varied in the direction of temperature change and its position on temperature axis. The data provided information on three traits, i.e. 1) covered distance (cm) = distance travelled during 1 °C change; 2) average speed (cm/s) = average locomotor speed during 1°C change; 3) body mass (g). The second file provides R script for data analysis.
DOI: 10.34740/kaggle/dsv/6134873
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7732393
title: Annual probability of extreme heat and drought events, derived from Lange et al 2020

description: The time series of extreme events given by Lange et al has been processed into an annual probability of occurrence by researchers at the University of Oxford, using the pipeline available online at https://github.com/nismod/infra-risk-vis/blob/45d8974c311067141ee6fcaa1321c7ecdaa59752/etl/pipelines/isimip/Snakefile - this is a draft dataset, used for visualisation in https://global.infrastructureresilience.org/ but not otherwise reviewed or published.<br> <br> If you use this, please cite: Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI 10.1029/2020EF001616<br> <br> This is shared under a CC0 1.0 Universal Public Domain Dedication (CC0 1.0) When using ISIMIP data for your research, please appropriately credit the data providers, e.g. either by citing the DOI for the dataset, or by appropriate acknowledgment.<br> <br> Annual probability of drought (soil moisture below a baseline threshold) or extreme heat (temperature and humidity-based indicators over a threshold) events on a 0.5° grid. 8 hydrological models forced by 4 GCMs under baseline, RCP 2.6 & 6.0 emission scenarios. Current and future maps in 2030, 2050 and 2080.<br> <br> The ISIMIP2b climate input data and impact model output data analyzed in this study are available in the ISIMIP data repository at ESGF, see https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input and https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output, respectively. More information about the GHM, GGCM, and GVM output data is provided by Gosling et al. (2020), Arneth et al. (2020), and Reyer et al. (2019), respectively.<br> <br> Event definitions are given in Lange et al, table 1. Land area is exposed to drought if monthly soil moisture falls below the 2.5th percentile of the preindustrial baseline distribution for at least seven consecutive months. Land area is exposed to extreme heat if both a relative indicator based on temperature (Russo et al 2015, 2017) and an absolute indicator based on temperature and relative humidity (Masterton & Richardson, 1979) exceed their respective threshold value.
description: Funding: This research received funding from the FCDO Climate Compatible Growth Programme. Disclaimer: The views expressed in this website do not necessarily reflect the UK government's official policies.
DOI: 10.4121/21864590.v2
title: Data underlying the research of Quality prediction of strawberries with RGB image segments

description: In this research, we validate our hypothesis of using in-field data that are acquirable via commodity hardware to obtain acceptable accuracies in predicting the quality attribute of strawberries. This dataset consists of images and segments of strawberries in the wild, their quality measurements, and climate data during cultivation.
DOI: 10.4121/21864590
title: Data underlying the research of Quality prediction of strawberries with RGB image segments

description: In this research, we validate our hypothesis of using in-field data that are acquirable via commodity hardware to obtain acceptable accuracies in predicting the quality attribute of strawberries. This dataset consists of images and segments of strawberries in the wild, their quality measurements, and climate data during cultivation.