cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22608199
title: Supplementary Material for: Population pharmacokinetic modeling to inform sertraline dosing optimization in patients with depression

description: Sertraline is one of the most prescribed antidepressants, but its pharmacokinetic (PK) properties are still not completely characterized. Using non-linear mixed-effects modeling (NONMEM) we examined factors influencing sertraline PK variability in outpatients with major depressive disorder. Blood samples from 53 male and female adults treated with sertraline orally were collected at a steady-state. Various demographic and clinical covariates were tested by stepwise regression procedure. We found that sertraline clearance is significantly influenced by serum concentrations of its main metabolite N-desmethylsertraline, whereas clearance of N-desmethylsertraline is affected by both creatinine clearance and drug daily dose. These results were confirmed by the reduction of points dispersion in goodness-of-fit plots for their predicted versus measured concentrations and with bootstrapping analyses. This finding can serve to inform sertraline dosing optimization, especially when changes in kidney function occur in treated individuals, to prevent adverse drug reactions and maximize therapeutic benefits.
DOI: 10.6084/m9.figshare.5028110
title: Raw Data

description: Raw data includes the inputs data for evaluating the proposed model. Those data are downloaded from WIND database, CSMAR database and Investing.com.<br>
DOI: 10.25582/data.2022-07.2548037/2175733
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292955/2175734
title: Data for EMSL Project 51383 from July 2021
DOI: 10.5281/zenodo.7545299
title: GIS Shapefiles for In Situ Geologic Map based on PMars 2020 Perseverance Rover Observations

description: These are shapefiles for in situ geologic maps illustrated in Crumpler et al., 2023. n Situ Geologic Context Mapping Transect on the Floor of Jezero Crater from Mars 2020 Perseverance Rover Observations. Journal of Geophysical Research-Planets, Mars 2020 Results Special Collection
DOI: 10.5281/zenodo.8296992
title: Data accompanying the article "Mapping Antarctic Crevasses and their Evolution with Deep Learning Applied to Satellite Radar Imagery"

description: Fracture and backscatter maps from June 2021 at 100m resolution, covering the Antarctic Ice Sheet. Maps showing estimated change in fracture density between January 2015 and July 2022 covering the Amundsen Sea Sector of West Antarctica, and accompanying uncertainty estimates at 1km resolution. Each of these four datasets is in GeoTiff form with CRS: EPSG:3031 - WGS 84 / Antarctic Polar Stereographic
DOI: 10.18462/iir.icr.2019.1874
title: Why and which Insulation materials for refrigerators !
DOI: 10.5281/zenodo.1436000
title: Drug Indications Extracted From Faers

description: This dataset contains drug indications extracted from the FDA Adverse Event Reporting System (FAERS).

Source code here: https://github.com/stuppie/faers

<strong>Method Outline</strong>


	Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see parser.py).
	Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see dedupe.py).
	Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See normalize_indications.py)
	Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don't match are run against rxnorm's approximate matching service, and are accepted if the score is higher than 67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See normalize_drugs.py)
	Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See get_indications.py)
DOI: 10.5281/zenodo.2563829
title: Bedrock radioactivity influences the rate and spectrum of mutation - Orthologous genes

description: Alignments of the 2490 orthologous genes used in the article "Natural Bedrock radioactivity influences the rate and spectrum of mutation" to estimate the mutational spectrum and synonymous substitution rate. To compute accurate synonymous substitution rate, we removed genes with short sequences (&lt;half of the alignment) and genes strongly supporting another phylogeny using ProfileNJ (Noutahi et al. 2016) with a bootstrap threshold of 90%, resulting in a subset of 769 genes listed in the file "List_769_1-to-1_orthologs_EvolutionRate.txt". Transcriptome paired-end reads used to define these orthologous genes have been deposited to the European Nucleotide Archive and are available under the study ID PRJEB14193. Sequences were aligned with Prank (Löytynoja & Goldman 2008) using a codon model and sites ambiguously aligned were removed with Gblocks (Castresana 2000).
description: {"references": ["L\u00f6ytynoja, A. & Goldman, N., 2008. Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science, 320(5883), pp.1632\u20131635.", "Castresana, J., 2000. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular biology and evolution, 17(4), pp.540\u2013552.", "Noutahi, E. et al., 2016. Efficient Gene Tree Correction Guided by Genome Evolution. PloS one, 11(8), p.e0159559."]}
DOI: 10.5281/zenodo.4071754
title: Data used in the article "Bedrock radioactivity influences the rate and spectrum of mutation"

description: Data used in the article entitled ‘Bedrock radioactivity influences the rate and spectrum of mutation’ by Nathanaëlle Saclier, Patrick Chardon, Florian Malard, Lara Konecny-Dupré, David Eme, Arnaud Bellec, Vincent Breton, Laurent Duret, Tristan Lefébure and Christophe J. Douady. Table 1: Description of sampling sites (species found and measurements of radioactivity). Table 2: Raw activity for each radionuclide in the selected sites Table 3: Computational methods to model the effective dose of radioactivity. Table 4: Corrected activity for each radionuclides. Table 5: Number of reads for each sequenced transcriptome Table 6: Mutation counts for each type of mutation, computed on all positions. Table 7: Mutation counts for each type of mutation, computed on third positions. Table 8: Relative frequency for each type of mutation, computed on all positions. Table 9: Relative frequency for each type of mutation, computed on third positions.
DOI: 10.5281/zenodo.8388661
title: Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters

description: The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by <em>Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications</em>, 2023.
DOI: 10.6084/m9.figshare.21896364
title: Supplementary Material for: Segmental Abnormalities of White Matter Microstructure in Primary Hypothyroidism Identified by Automated Fiber Quantification

description: Introduction: Hypothyroidism leads to impaired white matter (WM) integrity, associated with cognitive/neuropsychiatric dysfunction. However, the specific segmental abnormalities of the fibers remain unexplored. Therefore, this study aimed to investigate whether the damage of the WM is limited to a specific segment or the entire bundle via diffusion metrics using automated fiber quantification. Methods: A cross-sectional study was conducted on 31 hypothyroid patients and 28 healthy controls. Thyroid-related hormone levels, cognitive/neuropsychiatric function, and diffusion tensor image data were collected and analyzed. Correlation and random forest analyses were also performed. Results: The mean fractional anisotropy (FA) values were reduced at the fiber tract level. The mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values were increased in several fiber tracts, i.e., cingulum cingulate (CC), anterior forceps of corpus callosum (CCF_A). Significant correlations were found between cognitive function and diffusion indicators such as the FA value of the left corticospinal tract and arcuate fasciculus (AF), the MD value of left CC, the RD value of left AF, the AD value of left CC, and CCF_A. The widespread microstructure disruption was spread on multiple specific segments of different tracts at the point-wise level. The random forest revealed that the accuracy of recognizing hypothyroid patients was 82.5%, with the anterior component of CCF_A having the most significant contribution. Conclusion: WM microstructural integrity impairments were found in multi-segments of the multiple fiber bundles in hypothyroidism, which might be a potential mechanism of the underlying neurocognitive decline and cerebral impairment. The CCF_A might serve as a neuro biomarker for early warning of cerebral impairment in hypothyroidism.
DOI: 10.6084/m9.figshare.23803281
title: Supplementary Material for: Germline mutations in 32 cancer susceptibility genes by Next-Generation Sequencing among breast cancer patients

description: Introduction: BRCA1/2 germline mutations are the most well-known genetic determinants for breast cancer. However, the distribution of germline mutations in non-BRCA1/2 cancer susceptibility genes in Chinese breast cancer patients is unclear. The association between clinical characteristics and germline mutations remains to be explored. Methods: Consecutive breast cancer patients from Peking University People's Hospital were enrolled. Clinical characteristics were collected and next-generation sequencing (NGS) was performed using blood samples of participants to identify pathogenic/likely pathogenic (P/LP) germline mutations in 32 cancer susceptibility genes including homologous recombination repair (HRR) genes. Results: A total of 885 breast cancer patients underwent the detection of germline mutations. 107 P/LP germline mutations of 17 genes were identified in 116 breast cancer patients including 79 (8.9%) in BRCA1/2 and 40 (4.5%) in 15 non-BRCA1/2 genes. PALB2 was the most frequently mutated gene other than BRCA1/2 but still relatively rare (1.1%). There were 38 novel P/LP germline variants detected. P/LP germline mutations in BRCA1/2 were significantly associated with onset age(P&lt;0.001), the family history of breast/ovarian cancer(P=0.010), and molecular subtype(P&lt;0.001), while correlated with onset age (P&lt;0.001), site of breast tumor (P=0.028) and molecular subtype (P&lt; 0.001) in HRR genes. Conclusions: The multiple-gene panel prominently increased the detection rate of P/LP germline mutations in 32 cancer susceptibility genes compared to BRCA1/2 alone. Onset younger than or equal to 45 years of age, bilateral and triple-negative breast cancer (TNBC) patients may be more likely to be recommended for detecting P/LP germline mutations in HRR genes.
DOI: 10.3886/icpsr09211.v3
title: Physical Violence in American Families, 1985
title: Version 3

description: Data were collected in this follow-up survey to compare estimates of the incidence of intrafamily physical violence with estimates obtained in an earlier survey, PHYSICAL VIOLENCE IN AMERICAN FAMILIES, 1976 (ICPSR 7733). The main component of this survey design was a national cross-sectional survey of adults in the United States who either (1) were currently married or living together, (2) were single parents with children under 18 in the household, or (3) had been married or had lived with a partner of the opposite sex within the past two years. Approximately two-thirds of American households met one of these three requirements at the time of the survey. Another objective of the survey was to generate comparisons of the incidence of intrafamily physical violence by race and ethnicity. Variables in this data collection include number of couples, single parents, and children in the household, respondent's race, ethnicity, sex, age, and marital status, number of persons in the household previously married, years lived in the community, and employment/occupation information.
description: The initial stage of sample construction required the development of a national area probability sample based upon the distribution of the adult population of the United States. Non-Hispanic Blacks, Hispanic Blacks, and non-Black Hispanics were oversampled.
description: Datasets: DS0: Study-Level Files DS1: Data File DS2: SAS Data Definition Statements
description: Adults and children under 18 in American families.
DOI: 10.5281/zenodo.1481223
title: Oct Volumes For Glaucoma Detection

description: OCT volumes used for the training and evaluation of a glaucoma detection classifier as described in

A feature agnostic approach for glaucoma detection in OCT volumes<br>
https://arxiv.org/abs/1807.04855

Volumes are in NumPy format. A viewer is available at https://github.com/maet3608/oct-viewer
DOI: 10.5281/zenodo.4737435
title: IconArt Dataset

description: The dataset IconArt dataset was introduced in the following paper : "Weakly Supervised Object Detection in Artworks" Gonthier et al. ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018. You can also find project page for the paper here. This dataset contains 5955 images (from WikiCommons) : a train set of 2978 images and a test set of 2977 images (for classification task). 1480 of the 2977 images are annotated with bounding boxes for 7 classes : ‘angel’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’, ‘ruins’,‘Saint_Sebastien’. The version 2 of the database contains 10 classes with the same images. The classes are ‘angel’,‘beard’,‘capital’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’,‘ruins’,‘Saint_Sebastien’,‘turban’.
description: This research was supported in part by the "IDI 2017" project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.
description: {"references": ["Gonthier et al. (2018) Weakly Supervised Object Detection in Artworks arXiv:1810.02569"]}
DOI: 10.25582/data.2021-02.2143442/2175737
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2548398/2175740
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547919/2175741
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548169/2175742
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292690/2175743
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-02.2148291/2175744
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547889/2175745
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2554995/2175746
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2150202/2175747
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547926/2175748
title: Data for EMSL Project 51383 from July 2022
DOI: 10.5281/zenodo.10071102
title: UTERINE MYOMA IN WOMEN
DOI: 10.25582/data.2021-02.2149098/2175754
title: Data for EMSL Project 51383 from February 2021
DOI: 10.5281/zenodo.10071101
title: UTERINE MYOMA IN WOMEN
DOI: 10.25582/data.2021-07.2292703/2175755
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547941/2175756
title: Data for EMSL Project 51383 from July 2022
DOI: 10.5281/zenodo.10071090
title: Gen dataset of polymer donor materials of organic solar cells

description: Repetitive units of polymers are used to represent polymer molecules. The fragments are generated from the experimental molecules. The framework is used to generate polymers. Specifically, BRICS is used to decompose existing molecules into constituent fragments. These substructures are then categorized into core (C) (MolWt > 500) and spacer (S) (MolWt < 500). These fragments are then recombined through BRICS to generate new molecules in the order S-C-S-C'.C and C' represent different fragments from the core. The BRICS dataset is then used as input to VAE, which is used to generate more diverse structures. The Gen database is obtained from SMILES generated by BRICS and VAE. The Gen database includes a total of ~3.9 million polymers extracted from existing donor materials, which can be used for the discovery of new high-performance donor materials.
DOI: 10.5281/zenodo.10071089
title: Gen dataset of polymer donor materials of organic solar cells

description: Repetitive units of polymers are used to represent polymer molecules. The fragments are generated from the experimental molecules. The framework is used to generate polymers. Specifically, BRICS is used to decompose existing molecules into constituent fragments. These substructures are then categorized into core (C) (MolWt > 500) and spacer (S) (MolWt < 500). These fragments are then recombined through BRICS to generate new molecules in the order S-C-S-C'.C and C' represent different fragments from the core. The BRICS dataset is then used as input to VAE, which is used to generate more diverse structures. The Gen database is obtained from SMILES generated by BRICS and VAE. The Gen database includes a total of ~3.9 million polymers extracted from existing donor materials, which can be used for the discovery of new high-performance donor materials.
DOI: 10.25582/data.2021-02.2150309/2175759
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-07.2292858/2175760
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547954/2175761
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2150193/2175763
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547996/2175767
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2148368/2175769
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2548076/2175772
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2148281/2175774
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2150275/2175775
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2548000/2175776
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548060/2175777
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2149117/2175779
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547877/2175781
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548087/2175782
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547895/2175786
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548017/2175787
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2554977/2175788
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548185/2175790
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292627/2175791
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-07.2292656/2175792
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2548140/2175793
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548067/2175805
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2149102/2175811
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547923/2175814
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548048/2175815
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292693/2175816
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-07.2292821/2175818
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-02.2149060/2175822
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2548006/2175823
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2150162/2175824
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2147520/2175825
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2147464/2175826
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2554949/2175827
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292702/2175828
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547872/2175829
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2144421/2175830
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-07.2292678/2175831
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2548136/2175832
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-07.2292684/2175833
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547992/2175834
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547904/2175835
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548148/2175836
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2147497/2175837
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2150247/2175838
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-07.2292692/2175839
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2548164/2175840
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2554969/2175841
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547933/2175843
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547986/2175844
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547999/2175845
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547912/2175846
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2548168/2175847
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2150240/2175849
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2548149/2175850
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2150291/2175851
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2148342/2175852
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2150246/2175853
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-07.2292810/2175854
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-07.2292664/2175855
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2021-07.2292840/2175858
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547960/2175859
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547884/2175862
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2547937/2175863
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2022-07.2554997/2175864
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2147470/2175865
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2022-07.2547909/2175866
title: Data for EMSL Project 51383 from July 2022
DOI: 10.25582/data.2021-02.2149101/2175867
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2148344/2175868
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2147463/2175869
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-02.2144414/2175871
title: Data for EMSL Project 51383 from February 2021
DOI: 10.25582/data.2021-07.2292653/2175872
title: Data for EMSL Project 51383 from July 2021
DOI: 10.25582/data.2022-07.2547870/2175873
title: Data for EMSL Project 51383 from July 2022