cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7590020
title: DataDescriptor sample metadata

description: This sample metadata is supplementary information to accompany the AmelHap DataDescriptor. In addition to the details provided in the AmelHap Metadata (https://doi.org/10.5281/zenodo.7030888), it reports the average depth and variant counts at each stage of filtering, indicates which samples share high identity-by-state (IBS) values, and which samples were used when testing genotype imputation.
DOI: 10.5255/ukda-sn-856044
title: Maternal Depression and Anxiety Disorders: Longitudinal Secondary Data Analysis, 2020-2022

description: In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.
DOI: 10.6084/m9.figshare.21982514.v1
title: Abundance of invertebrate taxa in intertidal stands dominated by seaweeds and mussels in Nova Scotia, Canada

description: This dataset describes the density (number of individuals per dm<sup>2</sup>) of invertebrate taxa found in replicate plots (100 cm<sup>2</sup> in area) fully covered by either canopy-forming algae (<em>Chondrus crispus</em>) or mussels (<em>Mytilus</em> spp.) in wave-exposed rocky intertidal habitats just outside of Duncans Cove, Nova Scotia, Canada, surveyed between late September and early October of 2021.
DOI: 10.6084/m9.figshare.21982514
title: Abundance of invertebrate taxa in intertidal stands dominated by seaweeds and mussels in Nova Scotia, Canada

description: This dataset describes the density (number of individuals per dm<sup>2</sup>) of invertebrate taxa found in replicate plots (100 cm<sup>2</sup> in area) fully covered by either canopy-forming algae (<em>Chondrus crispus</em>) or mussels (<em>Mytilus</em> spp.) in wave-exposed rocky intertidal habitats just outside of Duncans Cove, Nova Scotia, Canada, surveyed between late September and early October of 2021.
DOI: 10.6084/m9.figshare.19735960
title: Relativistic Effects on Triple Black Holes II The Influence of Spin in Burrau’s Problem. 1-3.

description: Data for Runs 1-3 of "Relativistic Effects on Triple Black Holes II: The Influence of Spin in Burrau’s Problem," by the authors.
DOI: 10.6084/m9.figshare.19735960.v1
title: Relativistic Effects on Triple Black Holes II The Influence of Spin in Burrau’s Problem. 1-3.

description: Data for Runs 1-3 of "Relativistic Effects on Triple Black Holes II: The Influence of Spin in Burrau’s Problem," by the authors.
DOI: 10.6084/m9.figshare.21954008
title: Long-term monitoring CO2 concentrations and fluxes from two German reservoirs

description: The dataset contains eight-year data of biweekly CO2 concentrations and underlying potential drivers from two adjacent reservoirs with contrasting trophic states in Germany. The parameters include biweekly concentrations and fluxes of CO2 and CH4, discharge of inflow and outflow, water residence time (WRT), water temperature, DO, pH, DOC, wind speed, alkalinity, TP, NO3, DIC, air temperature, rainfall.
DOI: 10.6084/m9.figshare.21954008.v1
title: Long-term monitoring CO2 concentrations and fluxes from two German reservoirs

description: The dataset contains eight-year data of biweekly CO2 concentrations and underlying potential drivers from two adjacent reservoirs with contrasting trophic states in Germany. The parameters include biweekly concentrations and fluxes of CO2 and CH4, discharge of inflow and outflow, water residence time (WRT), water temperature, DO, pH, DOC, wind speed, alkalinity, TP, NO3, DIC, air temperature, rainfall.
DOI: 10.3886/e175582v1-132184
title: Data and Code for: Social Media and Mental Health

description: The diffusion of social media coincided with a worsening of mental health conditions among adolescents and young adults in the United States, giving rise to speculation that social media might be detrimental to mental health. In this paper, we provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. Our analysis couples data on student mental health around the years of Facebook's expansion with a generalized difference-in-differences empirical strategy. We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression. We also find that, among students predicted to be most susceptible to mental illness, the introduction of Facebook led to increased utilization of mental healthcare services. Lastly, we find that, after the introduction of Facebook, students were more likely to report experiencing impairments to academic performance resulting from poor mental health. Additional evidence on mechanisms suggests that the results are due to Facebook fostering unfavorable social comparisons.
DOI: 10.5281/zenodo.6560383
title: Raw Data for IntoValue Dataset

description: This data deposit includes large raw data used for the "IntoValue" dataset, which underlies several projects at the QUEST Center for Responsible Research in the Berlin Institute of Health (BIH) @ Charité. An initial version of the IntoValue dataset is available in Zenodo: https://doi.org/10.5281/zenodo.5141342. Based on this initial version, the dataset is actively developed and maintained in GitHub: https://github.com/maia-sh/intovalue-data. This Zenodo deposit serves to store large raw data files for individual trials and are used in that GitHub repository. These data are deposited for computational reproducibility and documentation; they are not intended to be used for additional projects and do not reflect the most current/accurate data available from each source. This deposit contains raw data from the following sources: <strong>PubMed</strong> (<code>pubmed.zip</code>): PubMed XML files are provided courtesty of the U.S. National Library of Medicine and were accessed via the Entrez Programming Utilities (E-utilities) API. The files were downloaded on 2021-08-15 and do not reflect the most current/accurate data available from NLM. The following scripts were used to download and create these files: get-pubmed.R; download-pubmed.R. <strong>German Clinical Trials Registry (DRKS)</strong> (<code>drks.zip</code>): DRKS does not provide an API and was webscrapped on 2021-08-15. The following scripts were used to download and create these XML files: get-drks.R; drks-functions.R <strong>ClinicalTrials.gov</strong> (<code>ctgov.zip</code>): ClinicalTrials.gov was accessed via the Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) via its PostgreSQL database API.The API was queried and CSV files were generated on 2021-08-15. The following scripts were used to download and create these files: get-aact.R; aact-functions.R. <strong>ClinicalTrials.gov 2018</strong> (<code>ctgov_2018.zip</code>): Additional trial data for 2018. ClinicalTrials.gov was accessed via the Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) via its PostgreSQL database API.The API was queried and CSV files were generated on 2021-10-06. The following scripts were used to download and create these files: get-aact.R; aact-functions.R.
description: This work was funded under a grant from the Federal Ministry of Education and Research of Germany (Bundesministerium für Bildung und Forschung - BMBF) [01PW18012].
DOI: 10.5519/qd.1ol9pysi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (13034 records)
DOI: 10.6073/pasta/f7a45325935bb69c36c29601e953a9e1
title: Freeze-thaw microcosm experiment data 2021

description: Warmer winters with less snowfall are increasing the frequency of soil freeze-thaw cycles across temperate regions. Soil microbial responses to freeze-thaw cycles vary and some of this variation may be explained by microbial conditioning to prior winter conditions, yet such linkages remain largely unexplored. We investigated how differences in temperature history influenced microbial community composition and activity in response to freeze-thaw cycles. We collected soil microbial communities that developed under colder (high elevation) and warmer (low elevation) temperature regimes in spruce-fir forests, then added each of these soil microbial communities to a sterile bulk-soil in a laboratory microcosm experiment. The inoculated high-elevation cold and low-elevation warm microcosms were subjected to diurnal freeze-thaw cycles or constant above-freezing temperature for 9 days. Then, all microcosms were subjected to a 7-day above-freezing recovery period. Overall, we found that the high-elevation cold community had, relative to the low-elevation warm community, a smaller reduction in microbial respiration (CO2 flux) during freeze-thaw cycles. Further, the high-elevation cold community, on average, experienced lower freeze-thaw-induced bacterial mortality than the warm community and may have partly acclimated to freeze-thaw cycles via increased lipid membrane fluidity. Respiration of both microbial communities quickly recovered following the end of the freeze-thaw treatment period and there were no changes in soil extractable carbon or nitrogen. Our results provide evidence that past soil temperature conditions may influence the responses of soil microbial communities to freeze-thaw cycles. The microbial community that developed under a colder temperature regime was more tolerant of freeze-thaw cycles than the community that developed under a warmer temperature regime, although both communities displayed some level of resilience. Taken together, our data suggest that microbial communities conditioned to less extreme winter soil temperatures may be most vulnerable to rapid changes in freeze-thaw regimes as winters warm, but they also may be able to quickly recover if mortality is low.
DOI: 10.25403/upresearchdata.21629636
title: Spectral power responsivity, wavelength calibrations and uncertainty calculation

description: Data for the spectral power responsivity calculation of detectors in the ultraviolet (UV) and visible to near-infrared radiation (IR) wavelength regions. Included also is the wavelength calibration data of the monochromator in the UV and visible to near-IR wavelength regions. Furthermore the data provided includes uncertainty analyses for spectral responsivity and wavelength calibrations.
DOI: 10.25403/upresearchdata.21629636.v1
title: Spectral power responsivity, wavelength calibrations and uncertainty calculation

description: Data for the spectral power responsivity calculation of detectors in the ultraviolet (UV) and visible to near-infrared radiation (IR) wavelength regions. Included also is the wavelength calibration data of the monochromator in the UV and visible to near-IR wavelength regions. Furthermore the data provided includes uncertainty analyses for spectral responsivity and wavelength calibrations.
DOI: 10.5519/qd.lc0ed1fk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (13 records)
DOI: 10.5281/zenodo.7589871
title: Sample sea ice topography at MOSAiC

description: This is a subset of reprojected and manually collocated data from the MOSAiC expedition October 2019 - October 2020. The original data is published in PANGEA or publicly available (satellite data). The data was collected over the Arctic drift ice over different times during the MOSAiC drift. To be able to collocate the data, the geographical coordinates were converted to a local coordinate system with the RV Polarstern at it's center and orientation fixed to the ship's heading in October 2019 (X,Y coordinates). In addition, small manual lateral shifts were applied to this local coordinates to achive the best collocation between the datasets. This collocation is based on field notes of observers that collected the ground data and is normally in the range of 10 m. These adjusted local coordinates were translated back into arbitrary geographical coordinates along the MOSAiC drift (lon0=0 and lat=80). The whole dataset with the original projections and time stamps is published at: -transect data (GEM-2 total thickness):<br> Hendricks, Stefan; Itkin, Polona; Ricker, Robert; Webster, Melinda; von Albedyll, Luisa; Rohde, Jan; Raphael, Ian; Jaggi, Matthias; Arndt, Stefanie (2022): GEM-2 quicklook total thickness measurements from the 2019-2020 MOSAiC expedition. PANGAEA, https://doi.org/10.1594/PANGAEA.943666 -Airborne laser data (Surface elevation):<br> Hutter, Nils; Hendricks, Stefan; Jutila, Arttu; Birnbaum, Gerit; von Albedyll, Luisa; Ricker, Robert; Haas, Christian (2022): Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1. <em>PANGAEA</em>, https://doi.pangaea.de/10.1594/PANGAEA.950896 -ROV Mutibeam data (Ice bottom draft):<br> Katlein, Christian; Anhaus, Philipp; Arndt, Stefanie; Krampe, Daniela; Lange, Benjamin Allen; Matero, Ilkka; Regnery, Julia; Rohde, Jan; Schiller, Martin; Nicolaus, Marcel (2022): Sea-ice draft during the MOSAiC expedition 2019/20. PANGAEA, https://doi.org/10.1594/PANGAEA.945846 Satellite data in this version is Sentinel 1 image (HH only): S1B_EW_GRDM_1SDH_20200313T114854_20200313T114954_020673_027304_FD59.SAFE
DOI: 10.5281/zenodo.7582590
title: Sample sea ice topography at MOSAiC

description: This is a subset of reprojected and manually collocated data from the MOSAiC expedition October 2019 - October 2020. The original data is published in PANGEA or publicly available (satellite data). The data was collected over the Arctic drift ice over different times during the MOSAiC drift. To be able to collocate the data, the geographical coordinates were converted to a local coordinate system with the RV Polarstern at it's center and orientation fixed to the ship's heading in October 2019 (X,Y coordinates). In addition, small manual lateral shifts were applied to this local coordinates to achive the best collocation between the datasets. This collocation is based on field notes of observers that collected the ground data and is normally in the range of 10 m. These adjusted local coordinates were translated back into arbitrary geographical coordinates along the MOSAiC drift (lon0=0 and lat=80). The whole dataset with the original projections and time stamps is published at: -transect data (GEM-2 total thickness):<br> Hendricks, Stefan; Itkin, Polona; Ricker, Robert; Webster, Melinda; von Albedyll, Luisa; Rohde, Jan; Raphael, Ian; Jaggi, Matthias; Arndt, Stefanie (2022): GEM-2 quicklook total thickness measurements from the 2019-2020 MOSAiC expedition. PANGAEA, https://doi.org/10.1594/PANGAEA.943666 -Airborne laser data (Surface elevation):<br> Hutter, Nils; Hendricks, Stefan; Jutila, Arttu; Birnbaum, Gerit; von Albedyll, Luisa; Ricker, Robert; Haas, Christian (2022): Merged grids of sea-ice or snow freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1. <em>PANGAEA</em>, https://doi.pangaea.de/10.1594/PANGAEA.950896 -ROV Mutibeam data (Ice bottom draft):<br> Katlein, Christian; Anhaus, Philipp; Arndt, Stefanie; Krampe, Daniela; Lange, Benjamin Allen; Matero, Ilkka; Regnery, Julia; Rohde, Jan; Schiller, Martin; Nicolaus, Marcel (2022): Sea-ice draft during the MOSAiC expedition 2019/20. PANGAEA, https://doi.org/10.1594/PANGAEA.945846 Satellite data in this version is Sentinel 1 image (HH only): S1B_EW_GRDM_1SDH_20200313T114854_20200313T114954_020673_027304_FD59.SAFE
DOI: 10.5281/zenodo.7579554
title: Nudging the tropical Indian Ocean Applied to the IPSL-CM6A-LR model - Cooling Experiments

description: This experiment applies monthly nudging of -2, -1, +1, and +2 degree C to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where we apply a constant nudging and refer to the experiments as TIOxC, where "x" is the monthly nudging value (as in Ferster et al., 2021). Additional experiments were simulated for a paper within the submission process (to be included and updated within future versions) including the "TIOc" and "C0C" experiments, where the TIOC nudges towards towards the time-mean climatology and the C0C nudges at each monthly time step towards the pre-industrial control's corresponding monthly value (similar to adding white noise at each time step). The datasets are included from the experiments to help share our results and reproducibility of the figures within our manuscripts, the original model output is stored on the TGCC computing machine Irene. All datasets are saved on this data repository are saved as annual means! These datasets represent the -2 and -1 degree C experiments. Please use the additional thredds link to access the full IPSL output for the experiments: https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M1C-TIO/1950-01-01/M1C-TIO1950-01/catalog.html https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M2C-TIO/1950-01-01/M2C-TIO1950-01/catalog.html The pre-industrial control from IPSL can be found on the CMIP6-ESGF servers and the TGCC computing machine Irene. Please contact us for additional explanations, datasets, or collaboration. *The datasets are created from the IPSL-CM6A-LR output files **This version serves as a preliminary version of the dataset for publication purposes, additional output is available and the experiment output exceeds the 50gb of storage offered through this platform (TGCC Irene). ***Contact: brady.ferster@locean.ipsl.fr or brady.ferster@yale.edu
DOI: 10.35078/fosw0s
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Teófilo Otoni| Município: Frei Inocêncio

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.6084/m9.figshare.21982424
title: Seurat SmartSeq A.10-12

description: <strong>seurat v3 object</strong> <br> <strong>ASSAYS:</strong> RNA: mRNA expression data <br> <strong>DIMENSIONALITY REDUCTION</strong> projected: Data was projected on the main AML dataset from Cohorts A and B. scanorama: Data was integrated with Scanorama, using the patient as Batch key umap: umap computed from Scanorama components <br> <strong>METADATA</strong> patient: Patient ct: Projected cell type (Triana et al., 2021) ct_simple: Simplified projected celltype pseudo_myel: Projected myeloid pseudotime (Triana et al., 2021) proj_cluster: Projected cluster from main AML dataset dormancy_score: score assessing the dormancy of cells based on Zhang 2021 and Cabezas-Wallscheid 2017 gene lists.
DOI: 10.6084/m9.figshare.21982454
title: Seurat more cells A.2

description: <strong>seurat v3 object</strong> <br> <strong>ASSAYS:</strong> ADT: Antibody expression data RNA: mRNA expression data <br> <strong>DIMENSIONALITY REDUCTION</strong> projAML: Data was projected on the main AML dataset from Cohorts A and B. pca: pca computed on the 2000 most variable features umapSimple: umap computed from first 15 principal components <br> <strong>METADATA</strong> patient: Patient ct: Projected cell type (Triana et al., 2021) ct_simple: Simplified projected celltype batch: Cells come either from total bone marrow or CD34+ enrichment using either FACS or MACS seurat_clusters: unsupervised clusters obtained running the standard Seurat workflow proj_cluster: Projected cluster from main AML dataset leukemia_prob: CloneTracer leukemia probability status: Binarized status (healthy, leukemic, unsure) clonal_probability: CloneTracer clonal probability clone: Binarized clone dormancy_score: score assessing the dormancy of cells based on Zhang 2021 and Cabezas-Wallscheid 2017 gene lists.
DOI: 10.6084/m9.figshare.21982490
title: Seurat more cells A.9

description: <strong>seurat v3 object </strong> <br> <strong>ASSAYS:</strong> ADT: Antibody expression data RNA: mRNA expression data <br> <strong>DIMENSIONALITY REDUCTION</strong> projAML: Data was projected on the main AML dataset from Cohorts A and B. pca: pca computed on the 2000 most variable features umap: umap computed from first 15 principal components <br> <strong>METADATA</strong> patient: Patient ct: Projected cell type (Triana et al., 2021) ct_simple: Simplified projected celltype batch: Cells come either from total bone marrow or CD34+ enrichment using FACS proj_cluster: Projected cluster from main AML dataset leukemia_prob: CloneTracer leukemia probability status: Binarized status (healthy, pre-leukemic, leukemic, unsure) dormancy_score: score assessing the dormancy of cells based on Zhang 2021 and Cabezas-Wallscheid 2017 gene lists.
DOI: 10.6084/m9.figshare.21982496
title: CloneTracer output

description: CloneTracer outputs <br> tar.gz file which contains for each patient a list with the following items: <br> patient: patient name M: matrix with mutant counts (mutationsxcells) N: matrix with reference counts (mutationsxcells) mutations_matrix: column names of M and N matrix with mutation names cell_barcode: rownames of M and N matrix trees: clonal hierarchies with the highest evidence according to CloneTracer. Binary encoding (0: abscence of mutation ; 1: presence of mutation) mutations_tree: column names of trees tree_indices: indices of selected trees over all potential_trees (only required for internal functions) clonal_prob: list of matrices with clonal probabilities corresponding to trees listed in trees slot selected_tree: tree selected for downstream analysis selected_clonal_probs: clonal probabilities of selected tree selected_tree_ind: index of selected tree ELBO: ELBO values for all trees stored in potential_trees slot. potential_trees: list of trees evaluated in the last inference step of the model. children and parent: only required for internal functions. <br> We provide costumised functions and example vignettes to visualize this type of data in https://github.com/veltenlab/CloneTracer/tree/master/clonal_inference/vignettes
DOI: 10.5281/zenodo.7590059
title: N-TIMP2 CD loop extension NGS data and analysis script

description: <strong>NGS data and analysis script for the YSD sorts described in Bonadio et al. "Using designed loop extension and combinatorial screening to enhance specificity of a broad matrix metalloproteinase inhibitor"</strong>
DOI: 10.5281/zenodo.7590058
title: N-TIMP2 CD loop extension NGS data and analysis script

description: <strong>NGS data and analysis script for the YSD sorts described in Bonadio et al. "Using designed loop extension and combinatorial screening to enhance specificity of a broad matrix metalloproteinase inhibitor"</strong>