cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21101944
title: Supplementary Material for: Factors, including clinical trial eligibility, associated with induction of third-line treatment for advanced gastric cancer

description: Introduction: Third-line chemotherapy has been suggested to improve survival in patients with gastric cancer. This study aimed to identify factors associated with the induction of third-line chemotherapy for advanced gastric cancer, focusing on patient eligibility for clinical trial. Methods: We retrospectively analyzed 335 patients treated for unresectable or recurrent gastric cancer between April 2009 and May 2020. The patients were grouped into those that met the key eligibility criteria for clinical trial (136 patients, 40.6%) and those that did not (199 patients, 59.4%) before receiving first-line chemotherapy. Results: The overall survival (OS) were 16.8 months (95%CI, 14.0–19.6) and 9.3 months (95%CI, 7.8–11.0) in the eligible and ineligible group, respectively. Multivariate analyses to identify the risk factors associated with the induction of third-line chemotherapy revealed ineligibility of clinical trial (OR 1.95; 95%CI 1.15–3.31), number of metastatic sites (OR 1.99; 95%CI 1.23–3.22), low albumin concentration (OR 2.24; 95%CI 1.14–4.38) and a lack of complete or partial response to first-line treatment (OR 1.85; 95%CI 1.05–3.26). Indeed, in responders to first-line treatment for ineligible patients, the median OS was 17.7 months (95%CI, 10.6–27.9), respectively. Conclusions: Treatment outcomes were different for those eligible for clinical trials and those who were not. However, this study suggested that patients who responded to first-line treatment have more favorable prognoses when treated with salvage chemotherapy, even if they were deemed ineligible for clinical trials.
DOI: 10.7294/21428619
title: Kansas Data from the Wildlife Viewer Survey

description: Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in Kansas. It contains: 1. Kansas Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. Kansas_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. Kansas_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets.
DOI: 10.7294/21428619.v1
title: Kansas Data from the Wildlife Viewer Survey

description: Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in Kansas. It contains: 1. Kansas Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. Kansas_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. Kansas_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets.
DOI: 10.5257/iea/wes/2022
title: World Energy Statistics (2022 Edition)

description: World Energy Statistics provides comprehensive world energy statistics on all energy sources - coal, gas, oil, electricity, renewables and waste. It covers energy supply and consumption for countries and regions, including all OECD countries, over 100 other key energy-producing and -consuming countries, as well as world totals and various regional aggregates. The dataset includes detailed tables by country in original units, and summary time-series on production, trade, and final consumption by sector.
DOI: 10.21227/64s5-q431
title: O-RAN experimental evaluation datasets

description: The Open Radio Access Network (O-RAN) Alliance's most recent innovations are propelling the evolution of RAN deployments, moving away from conventionally closed and specialized hardware implementations and toward virtualized instances running over shared platforms and distinguished by open interfaces. Such progressive decoupling of radio software components from the hardware is paving the road for future efficient and cost-effective RAN deployments. However, there are still a lot of open challenges before O-RAN networks can be successfully deployed. Many of these open challenges include real-time network parameter configuration to maximize performance, providing enough computing resources required for vRAN deployment, and how to share processing units among multiple vRAN instances reliably. To shed light on these aspects, we have released the following datasets, which collect experimental data from published papers that research O-RAN deployments for 5G networks. It includes rich data on computing consumption, energy consumption, and other aspects that can help other researchers to develop a new solution for O-RAN deployments.
DOI: 10.5281/zenodo.7096491
title: CESM Simulation pi_b1850_paleocab (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096490
title: CESM Simulation pi_b1850_paleocab (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096473
title: CESM Simulation datatm21ka_c211123 (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096472
title: CESM Simulation datatm21ka_c211123 (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096499
title: CESM Simulation datatmcold_c220426 (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096498
title: CESM Simulation datatmcold_c220426 (Epitomic Data)

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.6084/m9.figshare.21101938.v1
title: Supplementary Material for: PROX1 was associated with LGR5 and Wnt signaling and contributed to poor prognosis in gastric cancer

description: Introduction: We investigated prospero homeobox protein-1 (PROX1) expression in gastric cancer (GC) as a prognostic marker and its relationship with LGR5 and Wnt/β-catenin activity in GC. Methods: A total of 196 cases of patients who underwent GC surgery were retrospectively collected and reviewed. PROX1, LGR5, Wnt3a, and β-catenin expression was evaluated by using immunohistochemistry. The relationship between PROX1 expression and clinicopathological features was also evaluated. Results: The PROX1 low-expression group had 105 patients (53.6%) and the high-expression group 91 patients (46.4%). For LGR5, 76 patients (38.8%) had low expression and 120 (61.2%) high expression. The PROX1 low-expression group was significantly younger (p = 0.0095), had more intestinal type (p = 0.014), and smaller tumor size (p = 0.013). The PROX1 high-expression group significantly correlated with high LGR5 expression (p &lt; .0001) and high Wnt3a expression (p = 0.012). There were more cases of postoperative recurrence in the PROX1 high-expression group (p = 0.013). Conclusion: Our findings show that PROX1 correlates with LGR5 and Wnt3a signaling in GC and has a poor prognosis with postoperative recurrence.
DOI: 10.22008/fk2/plnueo/fyqslq
title: basalmelt.csv

description: Basal melt rates as csv file
DOI: 10.22008/fk2/plnueo/nljhu7
title: basalmelt.mat

description: Basal melt rates as MATLAB file
DOI: 10.22008/fk2/plnueo/bnxg0b
title: basalmelt.nc

description: Basal melt rates as NetCDF file
DOI: 10.22008/fk2/plnueo/ijfemy
title: make_figures_karlssonetal2021.py

description: Calculating and plotting the basal melt rates from the heat sources.
DOI: 10.22008/fk2/plnueo/l7usxp
title: basalmelt_Karlssonetal2021_updated2022.nc

description: Updated (2022) basal melt rates from geothermal flux and friction using new geothermal maps and basal state.
DOI: 10.22008/fk2/plnueo/npkvss
title: heatsources.nc

description: This file contains the individual heat sources used for calculating the basal melt.
DOI: 10.22008/fk2/plnueo/wjhrui
title: heatsources_Karlssonetal2021_updated2022.nc

description: Updated (2022) geothermal flux and friction heat.
DOI: 10.22008/fk2/plnueo/13rdvb
title: basalmelt_upd2022_ascii.csv

description: Updated (2022) basal melt rates from geothermal flux and friction using new geothermal maps and basal state.
DOI: 10.22008/fk2/plnueo/trlsmv
title: basalmelt_mat_upd2022.mat

description: Updated (2022) basal melt rates from geothermal flux and friction using new geothermal maps and basal state.
DOI: 10.22008/fk2/plnueo/y6vfmx
title: README_upd2022.txt

description: Description of updated data files
DOI: 10.5281/zenodo.7118511
title: CLM Standalone Simulation badsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7118510
title: CLM Standalone Simulation badsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5519/qd.e5v8pl3g
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (40 records)
DOI: 10.5281/zenodo.7096463
title: CLM Standalone Simulation rassector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096462
title: CLM Standalone Simulation rassector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7267104
title: DATA for 3D PIC simulation of MS waves

description: Data for the paper of Excitation of magnetosonic waves in the Earth's dipole magnetic field: 3D PIC simulation
DOI: 10.5281/zenodo.7267103
title: DATA for 3D PIC simulation of MS waves

description: Data for the paper of Excitation of magnetosonic waves in the Earth's dipole magnetic field: 3D PIC simulation
DOI: 10.5281/zenodo.7096451
title: CLM Standalone Simulation pujsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096450
title: CLM Standalone Simulation pujsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096447
title: CLM Standalone Simulation nspsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096446
title: CLM Standalone Simulation nspsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096445
title: CLM Standalone Simulation nsisector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096444
title: CLM Standalone Simulation nsisector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.6084/m9.figshare.21391575.v1
title: NGS data for DNA-based NGS for fusion-gene detection

description: The identification of the genomic sequence of the breakpoint flanking regions of the fusion gene translocation was sequenced by next-­generation sequencing (NGS).
DOI: 10.5281/zenodo.7096438
title: CLM Standalone Simulation ngasector_cold_exper22

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096437
title: CLM Standalone Simulation ngasector_cold_exper22

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.34740/kaggle/dsv/4427386
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.7096434
title: CLM Standalone Simulation mausector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096433
title: CLM Standalone Simulation mausector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096429
title: CLM Standalone Simulation knysector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096428
title: CLM Standalone Simulation knysector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.25446/oxford.21436059
title: The Right to Play: Interviews with Experts in the Field

description: The data includes interview transcripts from eight 'experts in the field', four covering the United Kingdom, and four covering Tanzania. The interviews include data on the experiences and perspectives of the experts on the realisation, implementation and incorporation of the right to play domestically. The data includes perspectives on the role of government, the challenges faced in supporting the realisation of the right to play, how challenges could/should be overcome, and the experience of lobbying government (local and national) to support the right to play.
DOI: 10.25446/oxford.21436059.v1
title: The Right to Play: Interviews with Experts in the Field

description: The data includes interview transcripts from eight 'experts in the field', four covering the United Kingdom, and four covering Tanzania. The interviews include data on the experiences and perspectives of the experts on the realisation, implementation and incorporation of the right to play domestically. The data includes perspectives on the role of government, the challenges faced in supporting the realisation of the right to play, how challenges could/should be overcome, and the experience of lobbying government (local and national) to support the right to play.
DOI: 10.5281/zenodo.7096425
title: CLM Standalone Simulation kinsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096424
title: CLM Standalone Simulation kinsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096421
title: CLM Standalone Simulation kilsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf<br>
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096420
title: CLM Standalone Simulation kilsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf<br>
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.34740/kaggle/dsv/4426624
title: Ukraine Conflict Twitter Dataset (54.41M tweets)
DOI: 10.5281/zenodo.7096415
title: CLM Standalone Simulation iztsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096414
title: CLM Standalone Simulation iztsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.23728/b2share.12c17379d9b443a693ec5a2e54abb8d7
title: uPOP clock: a microcell atomic clock based on double-resonance Ramsey scheme

description: This is the data for the paper "uPOP clock: a microcell atomic clock based on double-resonance Ramsey scheme"
DOI: 10.5281/zenodo.7096400
title: CLM Standalone Simulation illsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7096399
title: CLM Standalone Simulation illsector_cold_exper22a

description: See Readme_Simulations_c221028.pdf
description: The creation and archiving of these simulations were funded by the US National Science Foundation's Sedimentary Geology and Paleobiology Program (EAR-1849754).
DOI: 10.5281/zenodo.7267251
title: RJaBi/charmBaryonData: Version 1, Draft 1

description: Charm Baryon Data This repository contains the data and plot-input files required to reproduce the figures in the charm parity paper Conda instructions: Install Environment <code>conda env create -f environment.yml</code> Activate/Use <code>conda activate charm</code> Update (w. new packages) Edit <code>environment.yml</code> Deactivate conda environment with <code>conda deactivate</code> Update conda environment with <code>conda env update -f=environment.yml</code> Run Instructions: change the scriptDir variable in main.sh and parityRatioRun.sho to where you have cloned the script repository https://github.com/RJaBi/charmBaryonScripts ensure that main.sh is executable (i.e. chmod o+x main.sh) This is neccessary for the subscript that runs the fits to work activate the charm conda environment Run Figures 1, 5-14 via ./main.sh This will likely take up to ~12 hours as it does all the fits Once the fits are made, the process is much quicker as the fits are saved Run Figures 2-4 via running parityRatioRun.sh This will likely take 12+ hours running the parityRatio plots off is slow, the inflection points are quick If you are doing this without running main.sh, you will need to make sure parityRatioRun unzip's the correlators The output will be in output/ but the figures actually used in the paper will be copied into paperPlots/ The input files for the fits are in baseToml. They are copied and modified by the run_fits script such that all combinations of operator, quarks and temperatures are done from the smaller subset of template toml files. Other input files are complete, based upon the relative paths defined. These are in completeToml Figure Details Figure 1 Plots the correlators plotG.py Figures 2-4 Does the parity-doubling R-ratio analysis gvarParityRatio.py plotInflectionPoints.py Figures 5-8 Does fits to the correlators and model averages simpleFit.py Figure 9 Plots the spectrum of masses against experiment plotSpectrum.py Figures 10-13 Plots the spectrum of masses as a function of temperature simplePlotEMP.py Figure 14 Considers the Gell-Mann-Okubo relation GellMannOkubo.py
DOI: 10.5281/zenodo.7267252
title: RJaBi/charmBaryonData: Version 1, Draft 1

description: Charm Baryon Data This repository contains the data and plot-input files required to reproduce the figures in the charm parity paper Conda instructions: Install Environment <code>conda env create -f environment.yml</code> Activate/Use <code>conda activate charm</code> Update (w. new packages) Edit <code>environment.yml</code> Deactivate conda environment with <code>conda deactivate</code> Update conda environment with <code>conda env update -f=environment.yml</code> Run Instructions: change the scriptDir variable in main.sh and parityRatioRun.sho to where you have cloned the script repository https://github.com/RJaBi/charmBaryonScripts ensure that main.sh is executable (i.e. chmod o+x main.sh) This is neccessary for the subscript that runs the fits to work activate the charm conda environment Run Figures 1, 5-14 via ./main.sh This will likely take up to ~12 hours as it does all the fits Once the fits are made, the process is much quicker as the fits are saved Run Figures 2-4 via running parityRatioRun.sh This will likely take 12+ hours running the parityRatio plots off is slow, the inflection points are quick If you are doing this without running main.sh, you will need to make sure parityRatioRun unzip's the correlators The output will be in output/ but the figures actually used in the paper will be copied into paperPlots/ The input files for the fits are in baseToml. They are copied and modified by the run_fits script such that all combinations of operator, quarks and temperatures are done from the smaller subset of template toml files. Other input files are complete, based upon the relative paths defined. These are in completeToml Figure Details Figure 1 Plots the correlators plotG.py Figures 2-4 Does the parity-doubling R-ratio analysis gvarParityRatio.py plotInflectionPoints.py Figures 5-8 Does fits to the correlators and model averages simpleFit.py Figure 9 Plots the spectrum of masses against experiment plotSpectrum.py Figures 10-13 Plots the spectrum of masses as a function of temperature simplePlotEMP.py Figure 14 Considers the Gell-Mann-Okubo relation GellMannOkubo.py
DOI: 10.5281/zenodo.6566909
title: Cellesce Z projection 2D dataset - Colorectal organoids

description: Cellesce Z projection 2D dataset - Colorectal organoids