cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20047136
title: Additional file 2 of CBX2 shapes chromatin accessibility promoting AML via p38 MAPK signaling pathway

description: Additional file 2: Supplementary Table 1. List of 223 common genes from comparison between unique regions by ATAC-seq and differentially upregulated (UP_REG DR) genes by RNA-seq in U937 shCBX2 cells. Supplementary Table 2. List of 25 genes included in the GO molecular function kinase binding (FDR q-value = 1.95 e-9). Supplementary Table 3. List of 334 common genes from comparison between unique regions by ATAC-seq and differentially downregulated (DOWN_REG DR) genes by RNA-seq in U937 shCBX2 cells. Supplementary Table 4. GO biological processes of 334 common genes from comparison between unique regions by ATAC-seq and the differentially downregulated (DOWN_REG DR) genes by RNA-seq in U937 shCBX2 cells. Supplementary Table 5. GO Molecular Function of 334 common genes from comparison between unique regions by ATAC-seq and differentially downregulated (DOWN_REG DR) genes by RNA-seq in U937 shCBX2 cells. Supplementary Table 6. GO KEGG pathways of 334 common genes from comparison between unique regions by ATAC-seq and differentially downregulated (DOWN_REG DR) genes by RNA-seq in U937 shCBX2 cells.
DOI: 10.6084/m9.figshare.20043191
title: CD34+ human bone marrow

description: scRNA-seq dataset early human hematopoiesis (CD34+ bone marrow cells) assayed using 10x chromium. Total cell number is 5,780. <br> This dataset contains raw spliced and unspliced counts estimated using the velocyto pipeline (La Manno et al., Nature 2018) that can be used to compute RNA velocity. Further, the dataset contains a precomputed Palantir pseudotime (Setty et al., Nature Biotechnology 2019). <br> Please cite the original publication: https://doi.org/10.1038/s41587-019-0068-4
DOI: 10.6084/m9.figshare.20043191.v1
title: CD34+ human bone marrow

description: scRNA-seq dataset early human hematopoiesis (CD34+ bone marrow cells) assayed using 10x chromium. Total cell number is 5,780. <br> This dataset contains raw spliced and unspliced counts estimated using the velocyto pipeline (La Manno et al., Nature 2018) that can be used to compute RNA velocity. Further, the dataset contains a precomputed Palantir pseudotime (Setty et al., Nature Biotechnology 2019). <br> Please cite the original publication: https://doi.org/10.1038/s41587-019-0068-4
DOI: 10.6084/m9.figshare.14318603
title: Zebrafish embryogenesis (axial mesoderm lineage)

description: scRNA-seq time-series dataset of zebrafish embryogenesis assayed using drop-seq, restricted to the axial mesoderm lineage. Contains 12 time-points spanning 3.3 - 12 hours past fertilization. Total cell number is 2,434. <br>We restricted data from the original study to cells labelled as `Lineage_Prechordal_Plate` and `Lineage_Notochord`. The `''gt_terminal_states'` annotation matches the labels shown in Supplementary Fig. 1 or the original study. For the `'lineages'` annotation, cells positive for both `Lineage_Prechordal_Plate` and `Lineage_Notochord` were labelled as 'Early Blastomeres`, cells positive for only one lineage were given the corresponding lineage label. This object contains a low-dimensional visualization computed using force-directed graph drawing in the original study. <br>Please cite the original publication: https://doi.org/10.1126/science.aar3131
DOI: 10.6084/m9.figshare.14318603.v2
title: Zebrafish embryogenesis (axial mesoderm lineage)

description: scRNA-seq time-series dataset of zebrafish embryogenesis assayed using drop-seq, restricted to the axial mesoderm lineage. Contains 12 time-points spanning 3.3 - 12 hours past fertilization. Total cell number is 2,434. <br>We restricted data from the original study to cells labelled as `Lineage_Prechordal_Plate` and `Lineage_Notochord`. The `''gt_terminal_states'` annotation matches the labels shown in Supplementary Fig. 1 or the original study. For the `'lineages'` annotation, cells positive for both `Lineage_Prechordal_Plate` and `Lineage_Notochord` were labelled as 'Early Blastomeres`, cells positive for only one lineage were given the corresponding lineage label. This object contains a low-dimensional visualization computed using force-directed graph drawing in the original study. <br>Please cite the original publication: https://doi.org/10.1126/science.aar3131
DOI: 10.6084/m9.figshare.14318603.v1
title: Zebrafish embryogenesis (axial mesoderm lineage)

description: scRNA-seq time-series dataset of zebrafish embryogenesis assayed using drop-seq, restricted to the axial mesoderm lineage. Contains 12 time-points spanning 3.3 - 12 hours past fertilization. Total cell number is 2,434. <br>We restricted data from the original study to cells labelled as `Lineage_Prechordal_Plate` and `Lineage_Notochord`. The `''gt_terminal_states'` annotation matches the labels shown in Supplementary Fig. 1 or the original study. For the `'lineages'` annotation, cells positive for both `Lineage_Prechordal_Plate` and `Lineage_Notochord` were labelled as 'Early Blastomeres`, cells positive for only one lineage were given the corresponding lineage label. This object contains a low-dimensional visualization computed using force-directed graph drawing in the original study. <br>Please cite the original publication: https://doi.org/10.1126/science.aar3131
DOI: 10.6084/m9.figshare.14866008
title: MEF to iPSC reprogramming

description: scRNA-seq time-series dataset of mouse embryonic fibroblast (MEF) reprogramming to induced pluripotent stem cells (iPSCs)assayed using 10x Genomics. Contains 39 time points spanning days 0-18 during reprogramming. Total cell number is 236,285.<br>Cells have been cultured in one of two conditions, either serum or 2i (see `serum` and `2i` fields in data object). Note that this dataset has already been normalized by total counts and log-transformed. We added the `cell_sets` annotation by slightly modifying and combining the pre-computed annotations, introducing a `MEF/other` category that didn't exist in the original data. <br>Please cite the original publication: https://doi.org/10.1016/j.cell.2019.01.006
DOI: 10.6084/m9.figshare.14866008.v1
title: MEF to iPSC reprogramming

description: scRNA-seq time-series dataset of mouse embryonic fibroblast (MEF) reprogramming to induced pluripotent stem cells (iPSCs)assayed using 10x Genomics. Contains 39 time points spanning days 0-18 during reprogramming. Total cell number is 236,285.<br>Cells have been cultured in one of two conditions, either serum or 2i (see `serum` and `2i` fields in data object). Note that this dataset has already been normalized by total counts and log-transformed. We added the `cell_sets` annotation by slightly modifying and combining the pre-computed annotations, introducing a `MEF/other` category that didn't exist in the original data. <br>Please cite the original publication: https://doi.org/10.1016/j.cell.2019.01.006
DOI: 10.6084/m9.figshare.20047200
title: Additional file 2 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 2: Table S2. Shortlist of 76 putative targets.
DOI: 10.6084/m9.figshare.20047200.v1
title: Additional file 2 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 2: Table S2. Shortlist of 76 putative targets.
DOI: 10.6084/m9.figshare.20047203
title: Additional file 3 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 3: Table S3. AML Surfaceome and gene expression values from healthy tissues.
DOI: 10.6084/m9.figshare.20047203.v1
title: Additional file 3 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 3: Table S3. AML Surfaceome and gene expression values from healthy tissues.
DOI: 10.6084/m9.figshare.20047206.v1
title: Additional file 4 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 4: Table S4. Antibody panel for flow cytometric validation.
DOI: 10.6084/m9.figshare.20047206
title: Additional file 4 of Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

description: Additional file 4: Table S4. Antibody panel for flow cytometric validation.
DOI: 10.5281/zenodo.6630392
title: Reporting Non-consensual Pornography: Clarity, Efficiency and Distress

description: Experimental Data supporting the paper: Reporting Non-consensual Pornography:<br> Clarity, Efficiency and Distress
DOI: 10.5281/zenodo.6630391
title: Reporting Non-consensual Pornography: Clarity, Efficiency and Distress

description: Experimental Data supporting the paper: Reporting Non-consensual Pornography:<br> Clarity, Efficiency and Distress
DOI: 10.34658/rdb.v6glcg/ssv8ek
title: Read Me.txt

description: :unav
DOI: 10.34658/rdb.v6glcg/d3t1tx
title: ERT.zip

description: :unav
DOI: 10.34658/rdb.v6glcg/nidnlb
title: Experimental Points.xlsx

description: :unav
DOI: 10.34658/rdb.v6glcg/bftewj
title: Fast Camera Analysis.zip

description: :unav
DOI: 10.34658/rdb.v6glcg/glr2js
title: Read Me.txt

description: :unav
DOI: 10.6084/m9.figshare.20047272
title: Additional file 1 of The dynamic cellular and molecular features during the development of radiation proctitis revealed by transcriptomic profiling in mice

description: Additional file 1. Excel file containing differential expression data for all genes across all comparison groups and time points.
DOI: 10.6084/m9.figshare.20047272.v1
title: Additional file 1 of The dynamic cellular and molecular features during the development of radiation proctitis revealed by transcriptomic profiling in mice

description: Additional file 1. Excel file containing differential expression data for all genes across all comparison groups and time points.
DOI: 10.34658/rdb.v6glcg
title: Electrical Resistance Tomography for Control Applications: Quantitative Study of the Gas-Liquid Distribution inside A Cyclone

description: Data linked to the article published in the Sensors journal (https://doi.org/10.3390/s20216069)
DOI: 10.6084/m9.figshare.20047268
title: Additional file 1 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 1: Table S1. Mean performance of 24 Ex-PVP inbred lines testcrosses and checks for grain yield and other agronomic traits evaluated under Striga -infested, Striga non-infested conditions and across multiple test locations
DOI: 10.6084/m9.figshare.20047271
title: Additional file 2 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 2: Table S2. Standard heterosis for grain yield, Striga damage, and emerged Striga count for 24 Ex-PVP inbred lines testcrosses and checks
DOI: 10.6084/m9.figshare.20047268.v1
title: Additional file 1 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 1: Table S1. Mean performance of 24 Ex-PVP inbred lines testcrosses and checks for grain yield and other agronomic traits evaluated under Striga -infested, Striga non-infested conditions and across multiple test locations
DOI: 10.6084/m9.figshare.20047271.v1
title: Additional file 2 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 2: Table S2. Standard heterosis for grain yield, Striga damage, and emerged Striga count for 24 Ex-PVP inbred lines testcrosses and checks
DOI: 10.6084/m9.figshare.20047274
title: Additional file 3 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 3: Table S3. General combining ability (GCA) effects of 24 Ex-PVP inbred lines for grain yield and other agronomic traits under artificial striga infestation, striga non-infested, and across multiple test locations
DOI: 10.6084/m9.figshare.20047274.v1
title: Additional file 3 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 3: Table S3. General combining ability (GCA) effects of 24 Ex-PVP inbred lines for grain yield and other agronomic traits under artificial striga infestation, striga non-infested, and across multiple test locations
DOI: 10.6084/m9.figshare.20047277
title: Additional file 4 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 4: Table S4. Mean grain yield, specific combining ability (SCA) effects, yield and SCA-based heterotic grouping and DArTag SNP-based groups of 24 Ex-PVP maize inbred lines under Striga -infested, Striga non-infested conditions and across multiple test locations
DOI: 10.6084/m9.figshare.20047277.v1
title: Additional file 4 of Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines

description: Additional file 4: Table S4. Mean grain yield, specific combining ability (SCA) effects, yield and SCA-based heterotic grouping and DArTag SNP-based groups of 24 Ex-PVP maize inbred lines under Striga -infested, Striga non-infested conditions and across multiple test locations
DOI: 10.6084/m9.figshare.20047287
title: Additional file 2 of Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection

description: Additional file 2: Table S1. Complete list of exclusion reasons and their frequencies. Table S2. Bivariate generalized linear mixed effect model on all reported models adjusted for covariates. Table S3. Bivariate generalized linear mixed effect model on most important model of each study adjusted for covariates. Table S4. Summary of the bivariate analysis on the all the reported models and its corresponding subgroup analyses. Subgroups marked with an asterix (*) do not have a large enough model sample size in order for the result to be reliable. Table S5. Summary of the bivariate analysis on the most important model of each study and its corresponding subgroup analyses. Subgroups marked with an asterix (*) do not have a large enough model sample size in order for the result to be reliable. Table S6. Summary of the univariate (log-DOR) analysis on all the reported models and its corresponding subgroup analysis. Table S7. Summary of the univariate analysis (log-DOR) on the most important model of each study and its corresponding subgroup analysis.
DOI: 10.6084/m9.figshare.20047287.v1
title: Additional file 2 of Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection

description: Additional file 2: Table S1. Complete list of exclusion reasons and their frequencies. Table S2. Bivariate generalized linear mixed effect model on all reported models adjusted for covariates. Table S3. Bivariate generalized linear mixed effect model on most important model of each study adjusted for covariates. Table S4. Summary of the bivariate analysis on the all the reported models and its corresponding subgroup analyses. Subgroups marked with an asterix (*) do not have a large enough model sample size in order for the result to be reliable. Table S5. Summary of the bivariate analysis on the most important model of each study and its corresponding subgroup analyses. Subgroups marked with an asterix (*) do not have a large enough model sample size in order for the result to be reliable. Table S6. Summary of the univariate (log-DOR) analysis on all the reported models and its corresponding subgroup analysis. Table S7. Summary of the univariate analysis (log-DOR) on the most important model of each study and its corresponding subgroup analysis.
DOI: 10.5676/10.5676/dwd_gpcc/clim_m_v2022_050
title: GPCC Climatology Version 2022 at 0.5° : Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historical Data 

description: The new GPCC Precipitation Climatology (V.2022) is focusing on the period 1951-2000 and consists of normals from ca. 86,000 stations. The climatology comprises normals collected by WMO (CLINOs), delivered by the countries to GPCC or calculated from time- series of monthly data (with at least 10 complete years of data) available in our database. In case that time series of sufficient length (more than 40 years) for the period 1951-2000 were not available from a specific station, then climatological normals have also been calculated for 30-year reference periods within the period 1951-2000 (for 1961-1990, 1951-1980 or 1971-2000) with at least 20 years of data. If even this was not possible, normals have been calculated for other 30-year periods 1931-1960, 1941-1970, 1981- 2010 or 1991-2020 or for any other period with at least 10 complete years of data.
This climatology is available on a regular latitude/longitude grid with a spatial resolution of 0.25° x 0.25°, 0.5° x 0.5°, 1.0° x 1.0°, and 2.5° x 2.5°. This GPCC product has been developed as a background field, for superposition of an interpolated anomaly field according to the period of interest. All other GPCC products utilize the background fields of the GPCC Climatology to grid the monthly data at the corresponding resolution with the anomaly interpolation method.

In addition, the climatological periods 1931-1960, 1941-1970, 1951-1980, 1961-1990, 1971-2000, 1981-2010, 1991-2020 are available for download. The periods were calculated with at least 20 complete years and consists of data from ca. 20,000-40,000 stations.
DOI: 10.5676/10.5676/dwd_gpcc/clim_m_v2022_100
title: GPCC Climatology Version 2022 at 1.0° : Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historical Data

description: The new GPCC Precipitation Climatology (V.2022) is focusing on the period 1951-2000 and consists of normals from ca. 86,000 stations. The climatology comprises normals collected by WMO (CLINOs), delivered by the countries to GPCC or calculated from time- series of monthly data (with at least 10 complete years of data) available in our database. In case that time series of sufficient length (more than 40 years) for the period 1951-2000 were not available from a specific station, then climatological normals have also been calculated for 30-year reference periods within the period 1951-2000 (for 1961-1990, 1951-1980 or 1971-2000) with at least 20 years of data. If even this was not possible, normals have been calculated for other 30-year periods 1931-1960, 1941-1970, 1981- 2010 or 1991-2020 or for any other period with at least 10 complete years of data.
This climatology is available on a regular latitude/longitude grid with a spatial resolution of 0.25° x 0.25°, 0.5° x 0.5°, 1.0° x 1.0°, and 2.5° x 2.5°. This GPCC product has been developed as a background field, for superposition of an interpolated anomaly field according to the period of interest. All other GPCC products utilize the background fields of the GPCC Climatology to grid the monthly data at the corresponding resolution with the anomaly interpolation method.

In addition, the climatological periods 1931-1960, 1941-1970, 1951-1980, 1961-1990, 1971-2000, 1981-2010, 1991-2020 are available for download. The periods were calculated with at least 20 complete years and consists of data from ca. 20,000-40,000 stations.
DOI: 10.5281/zenodo.6630145
title: WMT-SLT SRF: Training data for the WMT shared task on sign language translation (poses)

description: These are Standard German daily news (Tagesschau) and Swiss German weather forecast (Meteo) episodes broadcast and interpreted into Swiss German Sign Language by hearing interpreters (among them, children of Deaf adults, CODA) via Swiss National TV (Schweizerisches Radio und Fernsehen, SRF) (https://www.srf.ch/play/tv/sendung/tagesschau-in-gebaerdensprache?id=c40bed81-b150-0001-2b5a-1e90e100c1c0). For a more extended description of the data, visit https://www.wmt-slt.com/data.
description: We additionally acknowledge funding through the Innosuisse Flagship "Inclusive Information and Communication Technologies" (IICT) (grant agreement no. PFFS-21-47).
DOI: 10.5281/zenodo.6630409
title: Fibvid dataset with multiple extracted features (both sparse and dense)

description: This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». <em>Telematics and Informatics</em> 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688. In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong> Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1] - A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1] - A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3] - A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4] <strong>References:</strong> [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2