cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9699131
title: Data from: Plant physical and chemical traits associated to herbivory in situ and under a warming treatment.

description: ============================================================<br>============================================================<br>============================================================<br><b><br></b><b>When using these datasets, please cite the following references: </b><br><br>Descombes, P., Kergunteuil, A., Glauser, G., Rasmann, S., & Pellissier, L. (2020). Plant physical and chemical traits associated with herbivory in situ and under a warming treatment. <i>Journal of Ecology</i>, <i>108</i>(2), 733-749.<br>Descombes, P., Kergunteuil, A., Glauser, G., Rasmann, S., & Pellissier, L. (2019): Data from: Plant physical and chemical traits associated to herbivory in situ and under a warming treatment.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.9699131<br>============================================================<br>============================================================<br>============================================================<br><b><br></b><b>Datasets descriptions:<br></b><b><br></b>Details on the data sampling are fully described in the aforementioned references.<br>Data_Herbivory_Palatabilty.txt: This dataset contains the plant traits and plant palatability (by Spodoptera) assessed for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by Spodoptera. <br><br> Data_Herbivory_Natural.txt: This dataset contains the plant traits and plant herbivory (by natural herbivores) assessed in plots for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by natural herbivores.<br><br>Data_Warming.treatement.txt: This dataset contains the plant traits and plant palatability (by spodoptera) assessed for samples collected in plots under an ambiant control and a warming treatement. This dataset was used for modeling the changes in plant traits between the control and warming treatments, and for assessing changes in plant palatability (by Spodoptera) associated with changes in leaf traits. <br><br>climherb_natural.herbivory_treatments.txt: This dataset contains the raw plant herbivory (by natural herbivores) assessed in plots for the plant species occurring on 3 grasslands in the Alps. <br>Columns are described in the respective readme files.<br>
DOI: 10.6084/m9.figshare.9699131.v5
title: Data from: Plant physical and chemical traits associated to herbivory in situ and under a warming treatment.

description: ============================================================<br>============================================================<br>============================================================<br><b><br></b><b>When using these datasets, please cite the following references: </b><br><br>Descombes, P., Kergunteuil, A., Glauser, G., Rasmann, S., & Pellissier, L. (2020). Plant physical and chemical traits associated with herbivory in situ and under a warming treatment. <i>Journal of Ecology</i>, <i>108</i>(2), 733-749.<br>Descombes, P., Kergunteuil, A., Glauser, G., Rasmann, S., & Pellissier, L. (2019): Data from: Plant physical and chemical traits associated to herbivory in situ and under a warming treatment.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.9699131<br>============================================================<br>============================================================<br>============================================================<br><b><br></b><b>Datasets descriptions:<br></b><b><br></b>Details on the data sampling are fully described in the aforementioned references.<br>Data_Herbivory_Palatabilty.txt: This dataset contains the plant traits and plant palatability (by Spodoptera) assessed for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by Spodoptera. <br><br> Data_Herbivory_Natural.txt: This dataset contains the plant traits and plant herbivory (by natural herbivores) assessed in plots for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by natural herbivores.<br><br>Data_Warming.treatement.txt: This dataset contains the plant traits and plant palatability (by spodoptera) assessed for samples collected in plots under an ambiant control and a warming treatement. This dataset was used for modeling the changes in plant traits between the control and warming treatments, and for assessing changes in plant palatability (by Spodoptera) associated with changes in leaf traits. <br><br>climherb_natural.herbivory_treatments.txt: This dataset contains the raw plant herbivory (by natural herbivores) assessed in plots for the plant species occurring on 3 grasslands in the Alps. <br>Columns are described in the respective readme files.<br>
DOI: 10.6084/m9.figshare.9699131.v4
title: Data from: Plant physical and chemical traits associated to herbivory in situ and under a warming treatment.

description: ============================================================<br>============================================================<br>============================================================<br><b><br></b><b>When using these datasets, please also cite the following reference: </b><br><br>Descombes, P., Kergunteuil, A., Glauser, G., Rasmann, S., & Pellissier, L. (2020). Plant physical and chemical traits associated with herbivory in situ and under a warming treatment. <i>Journal of Ecology</i>, <i>108</i>(2), 733-749.<br>============================================================<br>============================================================<br>============================================================<br><b><br></b><b>Datasets descriptions:<br></b><b><br></b>Details on the data sampling are fully described in the aforementioned references.<br>Data_Herbivory_Palatabilty.txt: This dataset contains the plant traits and plant palatability (by Spodoptera) assessed for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by Spodoptera. <br><br> Data_Herbivory_Natural.txt: This dataset contains the plant traits and plant herbivory (by natural herbivores) assessed in plots for the plant species occurring on 3 grasslands in the Alps. This dataset was used for modeling the herbivory by natural herbivores.<br><br>Data_Warming.treatement.txt: This dataset contains the plant traits and plant palatability (by spodoptera) assessed for samples collected in plots under an ambiant control and a warming treatement. This dataset was used for modeling the changes in plant traits between the control and warming treatments, and for assessing changes in plant palatability (by Spodoptera) associated with changes in leaf traits. <br><br>Columns are described in the respective readme files.<br>
DOI: 10.6084/m9.figshare.13548974
title: To be Deleted

description: Self-consistent inductive electric field<br>
DOI: 10.6084/m9.figshare.13548974.v2
title: To be Deleted

description: Self-consistent inductive electric field<br>
DOI: 10.6084/m9.figshare.13548974.v1
title: Eiy_51_24_46.out

description: Self-consistent inductive electric field<br>
DOI: 10.6084/m9.figshare.12765797
title: Data --- Self-Consistent Inductive Electric Field

description: Data --- Self-Consistent Inductive Electric Field
DOI: 10.6084/m9.figshare.12765797.v1
title: Data --- Self-Consistent Inductive Electric Field

description: Data --- Self-Consistent Inductive Electric Field
DOI: 10.25934/00004363.1
title: IPD datapackage for study 'Phase III Study of Gemcitabine Plus Paclitaxel Versus Paclitaxel in Patients With Unresectable, Locally Recurrent or Metastatic Breast Cancer' and research request: 'Predictors of therapeutic and adverse effects of medicines used in the treatment of breast cancer'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.6084/m9.figshare.13549070
title: Genomic and anatomical comparisons of skin support independent adaptation to life in water by cetaceans and hippos

description: Nexus alignment files for skin genes that are inactivated in cetaceans and hippos.
DOI: 10.26208/1f6w-3n24
title: Electrical Discharges Produce Prodigious Amounts of Hydroxyl and Hydroperoxyl Radicals

description: Reaction with the hydroxyl radical (OH) is often the first step in the removal of many atmospheric pollutants. The nitrogen oxides (NOx) generated by lightning can increase the amount of HOx (HOx = OH + HO2) present in the atmosphere, but direct HOx production from lightning has never been investigated in the laboratory. In this laboratory study, prodigious amounts of HOx were generated by both visible and subvisible electrical discharges over ranges of pressure and water vapor mixing ratios relevant to the troposphere. Also measured were NO, total nitrogen oxides (NOx), ozone (O3), and OH exposure, which is the integral of the hydroxyl radical concentration over time. HOx and OH exposure were approximately independent of pressure from 360 to 970 hPa and only slightly increased as water vapor increased from 1000 to 8000 parts per million volume (ppmv), while NOx was approximately independent of both pressure and water vapor over the same ranges. These laboratory measurements of excessive HOx and OH exposure are similar to measurements of electrically-generated HOx discovered in electrified anvil clouds during a 2012 airborne study, thus demonstrating the relevance of these laboratory results to the atmosphere and the importance of understanding the LHOx contribution to atmospheric oxidation.
DOI: 10.17632/349rpsdk8p.1
title: Data for: Sapphire-bearing magmatic rocks trace the boundary between paleo-continents: a case study of Ilmenogorsky alkaline complex, Uralian collision zone of Russia

description: These two MS.xlsx files contain the raw data on LA-ICP-MS trace element chemistry of corundums and zircons, U-Pb LA-ICP-MS zircon dating, Rb-Sr and Sm-Nd TIMS isotopy of syenite pegmatites, and SIMS oxygen isotopic measurements of corundum and zircon.
DOI: 10.17632/sxpybkn422.1
title: Data for: How 17O excess in clumped isotope reference frame materials and ETH standards affects reconstructed temperature.

description: Equilibrations_Log_v2.2.csv: Summary of preparatory line and mass spectrometer data for equilibrations. Columns titles describe the following: samplename: full sample name shortname: short name g2bkgnd: preparatory line background pressure O2_initial_pressure_warm_predicted: initial O2 pressure, estimated for room temperature. CO2_initial_pressure_predicted: initial CO2 pressure, estimated for room temperature. equil_starting_pressure: pressure at beginning of equilibration O2_CO2_ratio: estimate of O2 to CO2 ratio. Note this value differs subtly from beta, which is calculated from mass spectrometer delta values O2_final_transfer_residual: residual pressure for transfer of O2 to manifold CO2_final_transfer_residual: residual pressure for transfer of CO2 to breakseal equil_duration: equilibration duration H2_O2_cleanse: flag if preparatory line was “cleaned” with H2 and O2 sampletype: flag for sample type O2in d17O vsmow: initial 17O of O2 O2in d18O vsmow: initial 18O of O2 O2in D17O vsmow: initial ∆17O of O2 CO2in d13C vpdb: initial 13C of CO2 CO2in d18O vsmow: initial 18O of CO2 CO2f d13C vpdb: final 13C of CO2 CO2f d13C std: standard deviation of final 13C of CO2 CO2f d18O vsmow: final 18O of CO2 CO2f d18O std: standard deviation of final 18O of CO2 O2f d17O vsmow: final 17O of O2 O2f d17O std: standard deviation of final 17O of O2 O2f d18O vsmow: final 18O of O2 O2f d18O std: standard deviation of final 18O of O2 O2f D17O vsmow: final ∆17O of O2 O2f D17O stderr: standard error of final ∆17O of O2 flag: flag for erroneous data carb17O_DD47_samples: Comparison of ∆47 calculated with the 17O correction parameters of Brand et al., (2010) (D47_Brand), Brand parameters as modified by Daëron et al., (2016) (D47_17OxsZero) and measured ∆17O of reference frame materials (D47_17OxsRG) for 2006 carbonates measured in a recent reference frame. carb17O_DD47_ETH: As in carb17O_DD47_samples, for only ETH standard data. The additional effect of carbonate ∆17O is reflected in D47_17OxsAll. 17Ocarb_ChemGeo.R: R script for all manuscript calculations and figures using the 3 files described above.
DOI: 10.5281/zenodo.4429217
title: MSN (Moesin); A Target Enabling Package

description: Moesin (MSN; membrane-organizing extension spike protein) contains a FERM domain (Four-point-1, Ezrin, Radixin, Moesin) that links transmembrane receptors such as CD44 to the actin cytoskeleton, in a manner regulated by phosphorylation and PIP2. A proteomic, post-mortem analysis of &gt;400 brains identified a protein co-expression module that is highly correlated with pathological and cognitive measures of Alzheimer’s disease (AD). Moesin and CD44 have emerged as key drivers in the module. MSN is highly expressed in microglia and brain endothelium (as well as several non-brain tissues). This TEP targets the CD44-MSN interaction, to test the hypothesis that inhibiting the CD44-MSN interaction would reverse harmful activities of microglia and provide beneficial outcomes for AD patients.
description: Future versions of this TEP will contain experimental data on the MSN TEP.
DOI: 10.5281/zenodo.4429232
title: MSN (Moesin); A Target Enabling Package

description: Moesin (MSN; membrane-organizing extension spike protein) contains a FERM domain (Four-point-1, Ezrin, Radixin, Moesin) that links transmembrane receptors such as CD44 to the actin cytoskeleton, in a manner regulated by phosphorylation and PIP2. A proteomic, post-mortem analysis of &gt;400 brains identified a protein co-expression module that is highly correlated with pathological and cognitive measures of Alzheimer’s disease (AD). Moesin and CD44 have emerged as key drivers in the module. MSN is highly expressed in microglia and brain endothelium (as well as several non-brain tissues). This TEP targets the CD44-MSN interaction, to test the hypothesis that inhibiting the CD44-MSN interaction would reverse harmful activities of microglia and provide beneficial outcomes for AD patients.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of November 2020. For more information about TEPs and the TEP Programme, please visit https://thesgc.org/tep.
DOI: 10.5281/zenodo.4429216
title: MSN (Moesin); A Target Enabling Package

description: Moesin (MSN; membrane-organizing extension spike protein) contains a FERM domain (Four-point-1, Ezrin, Radixin, Moesin) that links transmembrane receptors such as CD44 to the actin cytoskeleton, in a manner regulated by phosphorylation and PIP2. A proteomic, post-mortem analysis of &gt;400 brains identified a protein co-expression module that is highly correlated with pathological and cognitive measures of Alzheimer’s disease (AD). Moesin and CD44 have emerged as key drivers in the module. MSN is highly expressed in microglia and brain endothelium (as well as several non-brain tissues). This TEP targets the CD44-MSN interaction, to test the hypothesis that inhibiting the CD44-MSN interaction would reverse harmful activities of microglia and provide beneficial outcomes for AD patients.
description: This document represents version 1 of the TEP datasheet and includes all updates on the project as of November 2020. For more information about TEPs and the TEP Programme, please visit https://thesgc.org/tep.
DOI: 10.5066/p9rtk75f
title: Hydrologic and soil data associated with selected vacant deconstruction lots in St. Louis, Missouri, 2018-2020

description: As the urban landscape and municipal infrastructure in U.S. cities changes in response to socio-economic conditions, so does the manner in which water cycles through these cities. The modulation of hydrologic processes (e.g., runoff, infiltration, evapotranspiration) by land use and land cover has implications for resilience, sustainability, and optimizing municipal service functions. The U.S. Geological Survey and U.S. Environmental Protection Agency, Office of Research and Development, collaborated to address the question of how Legacy (standard) and recent (Urban Greening Program or "Green") vacant lots ? each the product of a certain approach to the widespread practice of demolition ? differ in terms of how they cycle water and the extent to which they play a role in regulating the resiliency of the landscape to rainfall forcing. The intent of the data collection effort was to build on baseline data and expand on monitoring and field work that can lead to guidance to St. Louis (and generalizable to other cities who use demolition to control blight and facilitate redevelopment) as they deploy improved demolition practices and explore potential differences in the hydrologic response of Legacy and Green vacant lots. The potential hydrologic responses of Legacy and Green demolitions result from differences in the approach. Legacy demolitions retain the structure foundation, but the walls are taken to 12 inches below grade and the floor of the foundation is broken into pieces less than 8 feet with cracks sufficient to allow drainage. Clean fill from multiple sources is placed on top of the Legacy demolitions to a depth of at least 6 inches. Green demolitions require full material removal including the foundation. There is a single source of alluvial fill material that is placed on top of the demolition site to a depth of at least 12 inches.
DOI: 10.5281/zenodo.4429231
title: Mtcars

description: A test data set.
DOI: 10.5281/zenodo.4429230
title: Mtcars

description: A test data set.
DOI: 10.5281/zenodo.4294189
title: Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis

description: Contents <strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses. <strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures. <strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN. <strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI). (1) <strong>moccasin_paper_env.yaml</strong> Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced. <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions. (2) <strong>FigureReproduction.zip</strong> Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures. (3) <strong>MOCCASIN_ExampleDataset.zip</strong> Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. (4) <strong>encode_corrected.zip</strong> Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment.
DOI: 10.5281/zenodo.4294188
title: Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis

description: Contents <strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses. <strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures. <strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN. <strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI). (1) <strong>moccasin_paper_env.yaml</strong> Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced. <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions. (2) <strong>FigureReproduction.zip</strong> Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures. (3) <strong>MOCCASIN_ExampleDataset.zip</strong> Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. (4) <strong>encode_corrected.zip</strong> Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment.