cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5642567
title: Dataset related to article "Preoperative [11C]methionine PET to personalize treatment decisions in patients with lower-grade gliomas"

description: This record contains data related to article "Preoperative [11C]methionine PET to personalize treatment decisions in patients with lower-grade gliomas" <strong>Background: </strong> PET with radiolabelled amino acids is used in the preoperative evaluation of patients with glial neoplasms. This study aimed to assess the role of [ 11C]methionine (MET) PET in assessing molecular features, tumour extent, and prognosis in newly-diagnosed lower-grade gliomas (LGGs) surgically treated. <strong>Methods: </strong> 153 patients with a new diagnosis of grade 2/3 glioma who underwent surgery at our Institution and were imaged preoperatively using [ 11C]MET PET/CT were retrospectively included. [ 11C]MET PET images were qualitatively and semiquantitatively analyzed using tumour-to-background ratio (TBR). Progression-free survival (PFS) rates were estimated using the Kaplan-Meier method and Cox proportional-hazards regression was used to test the association of clinicopathological and imaging data to PFS. <strong>Results: </strong> Overall, 111 lesions (73%) were positive, while thirty-two (21%) and ten (6%) were isometabolic and hypometabolic at [ 11C]MET PET, respectively. [ 11C]MET uptake was more common in oligodendrogliomas than IDH-mutant astrocytomas (87% vs 50% of cases, respectively). Among [ 11C]MET-positive gliomas, grade 3 oligodendrogliomas had the highest median TBRmax (3.22). In 25% of patients, PET helped to better delineate tumour margins compared to MRI only. In IDH-mutant astrocytomas, higher TBRmax values at [ 11C]MET PET were independent predictors of shorter PFS. <strong>Conclusions: </strong> This work highlights the role of preoperative [ 11C]MET PET in estimating the type, assessing tumour extent, and predicting biological behaviour and prognosis of LGGs. Our findings support the implementation of [ 11C]MET PET in routine clinical practice to better manage these neoplasms.
DOI: 10.6084/m9.figshare.19525354.v1
title: Datasets.rar

description: For the data, there are some datasets about berthing, tracking and velocity function of the unamnned surface vehicle.
DOI: 10.6084/m9.figshare.19433318.v1
title: Effect of Rhizobium inoculation on growth of common bean in low fertility tropical soil amended with phosphorus and lime

description: Raw data of two trials conducted in low-fertility tropical soils at the smallholder farm-scale in Madagascar to assess the effects of Rhizobium inoculation together with inputs of P and lime on the growth of the common bean.<br><br><br>
DOI: 10.25345/c5jq0sz8n
title: MassIVE MSV000089208 - KDM4C histone demethylase connects chromatin remodeling and metabolism in triple-negative breast cancer
title: Li Z, Peluffo G, Stevens L, Qiu X, Egri S, Papanastasiou M, Kingston N, D'Santos CS, Evans K, Clement K, Temko D, Ekram M, Kales SC, Rai G, Lal-Nag M, Maloney DJ, Jadhav A, Michor F, Meissner A, Carroll JS, Freedman M, Long H, Jaffe JD, Polyak K. 2022. Triple-negative breast cancer (TNBC) is a highly aggressive disease with inferior prognosis necessitating the discovery of novel actionable targets. Here, we show that KDM4C, a histone demethylase amplified in a subset of TNBC, is a driver of TNBC tumor growth. Integrative multi-omic analysis demonstrated that KDM4C inhibition triggers chromatin and transcriptomic remodeling without substantial changes of its canonical substrates H3K9me3 and H3K36me3. Rather KDM4C loss caused proteolytic cleavage of histone H3 mediated by cathepsin L (CTSL) recruited to the chromatin by GRHL2. Metabolomic profiling showed reduced glutathione levels following KDM4C inhibition partly due to a decrease in glutamate-cysteine ligase (GCLC) leading to increased reactive oxygen species and activation of CTSL. The expression of KDM4C and GCLC correlated in TNBC and combined targeting of KDM4C and GSH axis improved response to cisplatin in TNBC. Our study reveals a novel, non-canonical role for KDM4C in TNBC that links metabolic and epigenetic profiles.
DOI: 10.5281/zenodo.6418136
title: Extreme hydrometeorological events, a challenge for gravimetric and seismology networks

description: “Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022. The data contains: 1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00: -Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects), To convert it into mm water: use factor -1/0.39 [mm/nm/s²] -Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer. Calibration factor: 838.86 [count/µm/s] -Rain [mm water]: data inferred from the weather radar -Flow [m³/s]: water entering the Eupen reservoir 2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996. 3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley 4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.
DOI: 10.5281/zenodo.5720116
title: Extreme hydrometeorological events, a challenge for gravimetric and seismology networks

description: “Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022. The data contains: 1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00: -Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects), To convert it into mm water: use factor -1/0.39 [mm/nm/s²] -Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer. Calibration factor: 838.86 [count/µm/s] -Rain [mm water]: data inferred from the weather radar -Flow [m³/s]: water entering the Eupen reservoir 2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996. 3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley 4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.
DOI: 10.5517/ccdc.csd.cc22rzwq
title: CCDC 1929684: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2019|CSD Communication|||
DOI: 10.6084/m9.figshare.19525327
title: Dataset of TMEM154 genotype and serological maedi-visna status of Sicilian ovine breeds.xlsx

description: The present dataset reports the results to investigate the association of p.Glu35Lys (E35K) polymorphism of the <i>transmembrane protein 15</i>4 (<i>TMEM154</i>) gene with the serological MV status of tested animals. In particular, 422 from 14 flocks across six provinces (Agrigento, Enna, Caltanissetta, Catania, Palermo, Trapani) of Sicily (South Italy) were serologically tested and genotyped for the TMEM154 E35K polymorphism using sequencing and TaqMan allelic discrimination methods.<br>
DOI: 10.6084/m9.figshare.19525327.v1
title: Dataset of TMEM154 genotype and serological maedi-visna status of Sicilian ovine breeds.xlsx

description: The present dataset reports the results to investigate the association of p.Glu35Lys (E35K) polymorphism of the <i>transmembrane protein 15</i>4 (<i>TMEM154</i>) gene with the serological MV status of tested animals. In particular, 422 from 14 flocks across six provinces (Agrigento, Enna, Caltanissetta, Catania, Palermo, Trapani) of Sicily (South Italy) were serologically tested and genotyped for the TMEM154 E35K polymorphism using sequencing and TaqMan allelic discrimination methods.<br>
DOI: 10.18154/rwth-2022-02243
title: Supplementary Information for Prof Leonhard’s group submission for HyDRA challenge

description: This document contains supplementary information of Prof Leonhard’s group submission to HyDRA challenge [DOI:10.26434/chemrxiv-2021-w8v42] on hydrogen-bonded clusters. Conformer search was conducted using CREST 2.11 [DOI:10.1039/C9CP06869D, DOI:10.1021/acs.jctc.9b00143, DOI:10.1039/d1sc00621e] at the GFN2-xTB [DOI:10.1021/acs.jctc.8b01176] level with default settings. The lowest energy conformer obtained was then optimized using Gaussian 16 at the b2plyp/aug-cc-pvtz level of theory with Grimme’s dispersion (gd3bj key word in Gaussian) [DOI:10.1063/1.3382344] and an extremely fine integral grid (int=grid=199974). Optimization was carried out using initial force constants from previous b2plyp/aug-cc-pvdz calculations. The frequency calculation at b2plyp/aug-cc-pvtz level was carried out with selected anharmonic modes. These modes were chosen based on visual inspection of normal modes mainly involving motion of the donor hydrogen. We optimized and calculated the frequencies for the clusters of water and one of the four molecules: Formaldehyde (FAH), Tetrahydrothiophene (THF), Tetrahydrofuran (THT) and Trifluroethan-1-ol (TFE).
DOI: 10.6084/m9.figshare.19291427
title: Data for "A Hierarchical Shape Description Approach and Its Application in Similarity Measurement of Polygon Entities"

description: This is the experimental data of the paper.
DOI: 10.6084/m9.figshare.19291427.v3
title: Data for "A Hierarchical Shape Description Approach and Its Application in Similarity Measurement of Polygon Entities"

description: This is the experimental data of the paper.
DOI: 10.6084/m9.figshare.19291427.v2
title: Data for "A Hierarchical Shape Description Approach and Its Application in Similarity Measurement of Polygon Entities"

description: This is the experimental data of the paper.
DOI: 10.5281/zenodo.6418086
title: PEMA databases

description: In this repo, the CREST and the RDPTClassifier oriented databases unsed in PEMA will be versioned. Training the CREST algorithm for the PR2 database output files have been added. The Silva 138 version has been added.
DOI: 10.6084/m9.figshare.19214289
title: Main dataset file

description: Main dataset file encoded as Python's pandas dataframe and saved in Python's pickle format.
DOI: 10.34740/kaggle/dsv/3416492
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.19281761.v1
title: data_in

description: <b>Data produced by analysis of the SMURPHS ensemble model output, as described in Boland et al 2022 (in prep). This data is required to reproduce the figures from this paper.</b><br>See https://github.com/emmomp/SMURPHS_OHC for:-notebooks to produce the figures from Boland et al 2022 (in prep) using this data - see below for which tar balls are needed for which figure.<br>-code to reproduce this data from the SMURPHS model output <br><br>To reproduce the figures, you need the following directories/files:- Figure 1 & Table 2: ohc_tseries, pic_data- Figure 2: ohc_tseries, pic_data, other_model_data- Figure 3: ohc_trends- Figures 4, S1, S2: ohc_xy- Figures 5, S3-S6: ohc_yz- Figure 6: ohc_xy- Figure 7: ohc_yz- Figure S7: amoc_tseries- Figure S8: SIE_SH.nc<br>The data files loaded were created using the python scripts in https://github.com/emmomp/SMURPHS_OHC/code/ as follows:- ohc_tseries: ohc_by_basin_depth.py<br>- pic_data: ohc_by_basin_depth_pic.py, ohc_pic_drift.py, ohc_xy_pic_drift.py, ohc_yz_pic_drift.py, ohc_xy_pic.py, ohc_yz_pic.py- ohc_trends: ohc_weightedtrends_obs.py, ohc_weightedtrends.py- ohc_xy: ohc_xy.py, ohc_xy_trends.py- ohc_yz: ohc_yz.py, ohc_yz_trends.py- amoc_tseries: calculate_AMOC.py- SIE_SH.nc : calc_SH_SIE.py<br>For the SMURPHS ensemble, see Dittus et al. 2020 (https://doi.org/10.1029/2019GL085806)<br><br>
DOI: 10.5281/zenodo.2540802
title: Getting "Fumpered": Classifying objects by what has been done to them

description: Dataset relative to the following publication: Schmidt, F., & Fleming, R. W. (in press). Getting "Fumpered": Classifying objects by what has been done to them. <em>Journal of Vision, 19(4):15</em>, 1-12<em>. </em>https://doi.org/10.1167/19.4.15 Each folder contains either the data or the experimental stimuli relative to the experiments and a text file with comments.
DOI: 10.5281/zenodo.2540801
title: Getting "Fumpered": Classifying objects by what has been done to them

description: Dataset relative to the following publication: Schmidt, F., & Fleming, R. W. (in press). Getting "Fumpered": Classifying objects by what has been done to them. <em>Journal of Vision, 19(4):15</em>, 1-12<em>. </em>https://doi.org/10.1167/19.4.15 Each folder contains either the data or the experimental stimuli relative to the experiments and a text file with comments.
DOI: 10.6084/m9.figshare.28785926
title: The processed files are required in this study

description: discovery cohort-cliniclal characteristics.xlsx : This file records the clinical information of the samples in the training set.discovery cohort-count.xlsx: count information for the samples in the training setdiscovery cohort-FPKM.xlsx: FPKM normalized values for the samples in the training setvalidation cohort-cliniclal characteristics.xlsx : This file records the clinical information of the samples in the validation set.validation cohort-count.xlsx: count information for the samples in the validation setvalidation cohort-FPKM.xlsx: FPKM normalized values for the samples in the validation setAUC-input.xlsx : This file is used to evaluate the AUC value of each characteristic gene in the training set
DOI: 10.6084/m9.figshare.28785926.v1
title: The processed files are required in this study

description: discovery cohort-cliniclal characteristics.xlsx : This file records the clinical information of the samples in the training set.discovery cohort-count.xlsx: count information for the samples in the training setdiscovery cohort-FPKM.xlsx: FPKM normalized values for the samples in the training setvalidation cohort-cliniclal characteristics.xlsx : This file records the clinical information of the samples in the validation set.validation cohort-count.xlsx: count information for the samples in the validation setvalidation cohort-FPKM.xlsx: FPKM normalized values for the samples in the validation setAUC-input.xlsx : This file is used to evaluate the AUC value of each characteristic gene in the training set
DOI: 10.15156/bio/sh0872459.10fu
title: SH0872459.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872460.10fu
title: SH0872460.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872461.10fu
title: SH0872461.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872462.10fu
title: SH0872462.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872463.10fu
title: SH0872463.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872464.10fu
title: SH0872464.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0872465.10fu
title: SH0872465.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.