cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11384241
title: DepMap 19Q4 Public

description: This dataset contains the results of Avana library CRISPR-Cas9 genome-scale knockout (prefixed with Achilles) as well as mutation, copy number and gene expression data (prefixed with CCLE) for cancer cell lines as part of the Broad Institute’s Cancer Dependency Map project. We have repackaged our fileset to include all quarterly-updating datasets produced by DepMap.<br>The Avana CRISPR-Cas9 genome-scale knockout data has expanded to include 689 cell lines, the RNAseq data includes 1249 cell lines, and the copy number data includes 1682 cell lines. Please see the README files for details regarding data processing pipeline procedures updates.<br>As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis for unrestricted use. For the latest datasets available, further analyses, and to subscribe to our mailing list visit https://depmap.org.<br>Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984. Some cell lines were process using copy number data based on the Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk.cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines. A detailed description of the pipelines and tool versions for CCLE expression can be found here: https://github.com/broadinstitute/gtex-pipeline/blob/v9/TOPMed_RNAseq_pipeline.md.<br>## V2 ChangesCCLE_fusions.csv and CCLE_fusions_unfiltered.csv were swapped in v1, they are correct now.<br>## V3 ChangesUACC62_SKIN_CJ1_RESISTANT has been removed from Public 19Q4 Achilles files due to an issue with fingerprinting. Values for this cell line have been NAed in the following files: Achilles_gene_effect.csv, Achilles_gene_effect_unscaled.csv, Achilles_gene_dependency.csv, Achilles_logfold_change.csv, Achilles_raw_readcounts.csv.<br>
DOI: 10.6084/m9.figshare.11384136
title: Building(s and) cities: Delineating urban areas with a machine learning algorithm - City & Employment Centre Boundaries (v1)

description: City and employment centre boundary delineations for Spain (except Basque Country and Navarra), based on building footprint from the Cadastre, and created using the A-DBSCAN algorithm.<br>GeoJSON polygons expressed in WGS-84 (ie. lon/lat), GeoPackage polygons expressed in ETRS89 / UTM zone 30N (EPSG:25830).<br>Files included in this dataset:<br>- <code>sp_blg_adbscan_city_boundaries_v1.geojson</code>: city boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_city_boundaries_v1.gpkg</code>: city boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>-<code> sp_blg_adbscan_emp_centre_boundaries_v1.geojson</code>: employment centre boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_emp_centre_boundaries_v1.gpkg</code>: employment centre boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>For further reference, please see original paper:<br>Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladecans-Marsal, E. (2020). "Building(s and) cities: Delineating urban areas with a machine learning algorithm". <i>Journal of Urban Economics.</i><br>
DOI: 10.6084/m9.figshare.11383671
title: Figure 6 Reproduction Files

description: Contains all the input and output files used to generate figure 5 IN_[stress].mat are the input files for each stress and the IFFL. These are the same as the ones found in Figure 3 and Figure 4. OUT_[stress]_X.mat are the output files run through MISC corresponding to each of the input files. There are five repeats each, denoted by X. IN_[stress]_permY.mat are the input files for each stress and the IFFL with the ordering of paired input and output shuffled. There are five repeats each, denoted by X. OUT_[stress]_permY.mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11383950
title: Figure 7 Reproduction Files

description: Contains all the input and output files used to generate figure 6 IN_[stress]_X_randY.m are the input files for each stress and the IFFL with X cells randomly selected from the complete set. There are five repeats each, denoted by Y. OUT [stress]_X_randY.m are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11384136.v1
title: Building(s and) cities: Delineating urban areas with a machine learning algorithm - City & Employment Centre Boundaries (v1)

description: City and employment centre boundary delineations for Spain (except Basque Country and Navarra), based on building footprint from the Cadastre, and created using the A-DBSCAN algorithm.<br>GeoJSON polygons expressed in WGS-84 (ie. lon/lat), GeoPackage polygons expressed in ETRS89 / UTM zone 30N (EPSG:25830).<br>Files included in this dataset:<br>- <code>sp_blg_adbscan_city_boundaries_v1.geojson</code>: city boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_city_boundaries_v1.gpkg</code>: city boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>-<code> sp_blg_adbscan_emp_centre_boundaries_v1.geojson</code>: employment centre boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_emp_centre_boundaries_v1.gpkg</code>: employment centre boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>For further reference, please see original paper:<br>Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladecans-Marsal, E. (2020). "Building(s and) cities: Delineating urban areas with a machine learning algorithm". <i>Journal of Urban Economics.</i><br>
DOI: 10.6084/m9.figshare.11383614
title: Figure 3 Reproduction Files

description: Contains all the input and output files used to generate figure 3. IN_IFFL.mat is the input file for MISC containing the synthetic incoherent feedforward loop data. OUT_IFFL.mat is the input data run through MISC.
DOI: 10.6084/m9.figshare.11383662
title: Figure 5 Reproduction Files

description: Contains all the input and output files used to generate figure 5 IN_[stress].mat are the input files for each stress (glucose, H2O2, KCl) OUT_[stress].mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11383671.v2
title: Figure 6 Reproduction Files

description: Contains all the input and output files used to generate figure 5 IN_[stress].mat are the input files for each stress and the IFFL. These are the same as the ones found in Figure 3 and Figure 4. OUT_[stress]_X.mat are the output files run through MISC corresponding to each of the input files. There are five repeats each, denoted by X. IN_[stress]_permY.mat are the input files for each stress and the IFFL with the ordering of paired input and output shuffled. There are five repeats each, denoted by X. OUT_[stress]_permY.mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11383662.v2
title: Figure 5 Reproduction Files

description: Contains all the input and output files used to generate figure 4 IN_[stress].mat are the input files for each stress (glucose, H2O2, KCl) OUT_[stress].mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11383671.v1
title: Figure 5 Reproduction Files

description: Contains all the input and output files used to generate figure 5 IN_[stress].mat are the input files for each stress and the IFFL. These are the same as the ones found in Figure 3 and Figure 4. OUT_[stress]_X.mat are the output files run through MISC corresponding to each of the input files. There are five repeats each, denoted by X. IN_[stress]_permY.mat are the input files for each stress and the IFFL with the ordering of paired input and output shuffled. There are five repeats each, denoted by X. OUT_[stress]_permY.mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11383614.v1
title: Figure 3 Reproduction Files

description: Contains all the input and output files used to generate figure 3. IN_IFFL.mat is the input file for MISC containing the synthetic incoherent feedforward loop data. OUT_IFFL.mat is the input data run through MISC.
DOI: 10.6084/m9.figshare.11383662.v1
title: Figure 4 Reproduction Files

description: Contains all the input and output files used to generate figure 4 IN_[stress].mat are the input files for each stress (glucose, H2O2, KCl) OUT_[stress].mat are the output files run through MISC corresponding to each of the input files.
DOI: 10.6084/m9.figshare.11382705
title: Giants_P18.csv

description: This sample contains 12 616 068 giant star candidates from Gaia DR2 with galactic latitude |b| &lt; 20 degrees. The selection procedure is described in Poggio et al. 2018.
DOI: 10.6084/m9.figshare.11382390
title: Metadata record for: A chromosome-level reference genome of the hornbeam, Carpinus fangiana

description: This dataset contains key characteristics about the data described in the Data Descriptor A chromosome-level reference genome of the hornbeam, Carpinus fangiana. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.11382390.v2
title: Metadata record for: A chromosome-level reference genome of the hornbeam, Carpinus fangiana

description: This dataset contains key characteristics about the data described in the Data Descriptor A chromosome-level reference genome of the hornbeam, Carpinus fangiana. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.11382147
title: diff_exp_genes_covered_by_GBSdata.xls

description: we have identified 8,111 genes with sex-biased expression, as shown in all_diff_exp_genes.xls. We next examined the total number of genes covered by the 8,639 SNPs in our dataset. In total, 1,454 genes were captured by 4,850 of our SNPs,as shown in diff_exp_genes_covered_by_GBSdata.xls
DOI: 10.6084/m9.figshare.11382390.v1
title: Metadata record for: A chromosome-level reference genome of the hornbeam, Carpinus fangiana

description: This dataset contains key characteristics about the data described in the Data Descriptor A chromosome-level reference genome of the hornbeam, Carpinus fangiana. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.11382117
title: all_diff_exp_genes.xls

description: We downloaded RNA-seq data of <i>N. vitripennis</i> from Gene Expression Omnibus (GEO) database with accession number GSE61156 to identified genes expressed deferentially between males and females. We aligned the reads to the <i>N. vitripennis</i> reference genome Nvit_2.1, and a total of 8,111 genes were covered by the RNA-seq data of all samples.
DOI: 10.6084/m9.figshare.11382147.v1
title: diff_exp_genes_covered_by_GBSdata.xls

description: we have identified 8,111 genes with sex-biased expression, as shown in all_diff_exp_genes.xls. We next examined the total number of genes covered by the 8,639 SNPs in our dataset. In total, 1,454 genes were captured by 4,850 of our SNPs,as shown in diff_exp_genes_covered_by_GBSdata.xls
DOI: 10.6084/m9.figshare.11382099
title: Appendix_sign._SNPs.xlsx

description: Wing morphology is an important trait underlying flight ability in winged insects. To investigate the genetic basis of wing morphology, we performed a genome-wide association study (GWAS) for additive and dominance effects on wing morphology and tibia length in an outbred population of the parasitoid wasp <i>Nasonia vitripennis</i>, using 1,230 individuals with genotypes for 8,639 SNPs. We found 46 SNPs in several distinct peaks across the genome with a genome-wide significant additive or dominance effect for wing morphology or tibia length.
DOI: 10.6084/m9.figshare.11382117.v1
title: all_diff_exp_genes.xls

description: We downloaded RNA-seq data of <i>N. vitripennis</i> from Gene Expression Omnibus (GEO) database with accession number GSE61156 to identified genes expressed deferentially between males and females. We aligned the reads to the <i>N. vitripennis</i> reference genome Nvit_2.1, and a total of 8,111 genes were covered by the RNA-seq data of all samples.
DOI: 10.6084/m9.figshare.11382054
title: Childhood acute poisoning in the Italian North-West area: a six-year retrospective study

description: A retrospective evaluation was performed on a 2012-2017 data registry. To describe the epidemiology of acute intoxication in children, to determine the responsible agents and the circumstances that led to the exposure in the different age classes, to report the type of treatment and the final outcome.<b>PATIENTS:</b> One thousand thirty children less than 14 years old accepted with a diagnosis of acute intoxication from Regina Margherita Hospital of Turin
DOI: 10.6084/m9.figshare.11382054.v1
title: Childhood acute poisoning in the Italian North-West area: a six-year retrospective study

description: A retrospective evaluation was performed on a 2012-2017 data registry. To describe the epidemiology of acute intoxication in children, to determine the responsible agents and the circumstances that led to the exposure in the different age classes, to report the type of treatment and the final outcome.<b>PATIENTS:</b> One thousand thirty children less than 14 years old accepted with a diagnosis of acute intoxication from Regina Margherita Hospital of Turin
DOI: 10.6084/m9.figshare.11380995
title: Supplementary Material for: A Survey of UK Centres on Low Iodine Diet Recommendations prior to Radioiodine Ablation Therapy for Differentiated Thyroid Cancer

description: <b><i>Background:</i></b> Guidelines suggest that a low iodine diet (LID) is advised prior to radioiodine ablation (RIA) for thyroid cancer. We aim to describe current practice regarding LID advice in the UK, determine uptake of the 2016 UK LID Working Group diet sheet and discover whether there are differences in practice. <b><i>Methods:</i></b> We used an online survey distributed between November 2018 and April 2019 to centres in the UK that administer <sup>131</sup>I. We asked questions on whether a LID is advised, for how long, how advice is presented, whether and how compliance is measured and whether treatment is delayed if LID advice is not followed. <b><i>Results:</i></b> Fifty-six clinicians from 47 centres that carry out RIA for thyroid cancer responded. Forty-four centres (94%) advise a LID prior to RIA, the majority for 14 days (82%). Two-thirds of the centres use the UK LID Working Group diet sheet. Patients are told to resume normal eating when <sup>131</sup>I is administered at 17 centres (39%), with 18 (41%) advising waiting for 24–48 h after administration. Most centres (95%) use only a simple question or do not assess compliance. Only 2 (5%) indicate that RIA would be delayed if someone said they had not followed LID advice. <b><i>Conclusions:</i></b> UK practice regarding LID prior to RIA for thyroid cancer is consistent with current guidelines, but non-adherence does not usually delay RIA. The UK Low Iodine Diet Working Group diet sheet is widely recognised and used. Practice could be improved by centres working to harmonise advice on when to restart a normal diet.
DOI: 10.6084/m9.figshare.11380995.v1
title: Supplementary Material for: A Survey of UK Centres on Low Iodine Diet Recommendations prior to Radioiodine Ablation Therapy for Differentiated Thyroid Cancer

description: <b><i>Background:</i></b> Guidelines suggest that a low iodine diet (LID) is advised prior to radioiodine ablation (RIA) for thyroid cancer. We aim to describe current practice regarding LID advice in the UK, determine uptake of the 2016 UK LID Working Group diet sheet and discover whether there are differences in practice. <b><i>Methods:</i></b> We used an online survey distributed between November 2018 and April 2019 to centres in the UK that administer <sup>131</sup>I. We asked questions on whether a LID is advised, for how long, how advice is presented, whether and how compliance is measured and whether treatment is delayed if LID advice is not followed. <b><i>Results:</i></b> Fifty-six clinicians from 47 centres that carry out RIA for thyroid cancer responded. Forty-four centres (94%) advise a LID prior to RIA, the majority for 14 days (82%). Two-thirds of the centres use the UK LID Working Group diet sheet. Patients are told to resume normal eating when <sup>131</sup>I is administered at 17 centres (39%), with 18 (41%) advising waiting for 24–48 h after administration. Most centres (95%) use only a simple question or do not assess compliance. Only 2 (5%) indicate that RIA would be delayed if someone said they had not followed LID advice. <b><i>Conclusions:</i></b> UK practice regarding LID prior to RIA for thyroid cancer is consistent with current guidelines, but non-adherence does not usually delay RIA. The UK Low Iodine Diet Working Group diet sheet is widely recognised and used. Practice could be improved by centres working to harmonise advice on when to restart a normal diet.
DOI: 10.6084/m9.figshare.11380569
title: Hypothyroidism during pregnancy

description: 怀孕期间甲状腺功能低下
DOI: 10.6084/m9.figshare.11379888
title: Supplementary Material for: Body Composition and Leptin/Ghrelin Levels during Lenvatinib for Thyroid Cancer

description: Weight loss is one of the most frequent adverse events during treatment with multikinase inhibitors, but scanty data are available on its extent and characteristics. This is the first assessment of the body composition by bioelectrical impedance analysis and of circulating leptin and ghrelin levels, in patients with advanced thyroid cancer before and at regular intervals during treatment with the tyrosine kinase inhibitor lenvatinib. Body mass index (BMI) decreased in all patients, with an average ∆ reduction of –6.4, –9.8, and –15.3% at 3, 6, and 12 months of treatment, respectively. Interestingly, in most patients, after the first year of treatment, BMI remained stable. In all patients, fat mass (FM) reduced more than fat-free mass, the highest decrement being of –60 and –16%, respectively. A decrease in the body cell mass, a parameter mainly due to muscle tissue, was observed only in patients with a vast baseline muscular mass. Total body water decreased in parallel to BMI. During treatment, leptin tightly paralleled the decrease of BMI values, consistent with the decrease in FM, whereas ghrelin levels increased upon BMI decrease. The loss of the FM accounts for the largest portion of BMI reduction during lenvatinib treatment. The increase in ghrelin could account for the BMI stabilization observed after 1 year of treatment. Nevertheless, oral nutritional supplements should be given as early as possible and athletic patients should be encouraged to maintain physical activity. In some circumstances, parenteral nutrition is required for the rehabilitation of these patients.
DOI: 10.6084/m9.figshare.11379777
title: Extended Data Table S6

description: Extended Data Table S6 for the mSWEEP manuscript.
DOI: 10.6084/m9.figshare.11379762
title: Extended Data Table S4

description: Extended Data Table S4 for the mSWEEP manuscript.
DOI: 10.6084/m9.figshare.11379777.v1
title: Extended Data Table S6

description: Extended Data Table S6 for the mSWEEP manuscript.
DOI: 10.6084/m9.figshare.11379756
title: Extended Data Table S3

description: Extended Data Table S3 for the mSWEEP manuscript.
DOI: 10.6084/m9.figshare.11379705
title: Extended Data Table S1

description: Extended Data Table S1 for the mSWEEP manuscript.
DOI: 10.6084/m9.figshare.11379933
title: sensitivity_analysis

description: Outputs from statistical analysis of USHCN and MODIS data, including sensitivity of primary production to precipitation and SPEI (mixed-model fixed and random effects), and uncertainty of sensitivity model parameters (RMSE). The ecoregion risk table summarizes findings on sensitivity and climate trends for each NEON domain.<br>