cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5063/f1x928rv
title: Reproductive consequences of shrub association, artificial herbivory, and supplemental pollination for Malacothrix glabrata in the Mojave Desert.

description: We tested the effects of shrub facilitation, two types of artificial herbivory (folivory and florivory), and supplemental pollination on floral production and seed set of Malacothrix glabrata in the Mojave Desert.
DOI: 10.5066/p97ney5y
title: Data describing infection status and movement ecology of North American waterfowl

description: These data, which support a USGS authored manuscript, describe how active and previous previous infection with avian influenza impacts the movement ecology of several wild waterfowl species that overwinter in California. Results varied by species and demonstrate that the relationships between avian influenza infection and wild bird movements are context- and species-dependent.
DOI: 10.5281/zenodo.2422746
title: Dirac gaugino benchmark points from arXiv:1812.09293

description: <strong>Benchmark points, in SUSY Les Houches Accord (SLHA) format</strong>, from the paper "LHC limits on gluinos and squarks in the minimal Dirac gaugino model", <strong>arXiv:1812.09293</strong>; for 2 TeV gluino and 2.6 TeV squark masses.

We also provide the <strong>UFO</strong> and <strong>SPheno models</strong> (exported from SARAH), and the SPheno <strong>input files</strong> for creating the DG benchmark points. For usage in SPheno, expand DiracGauginos_SPheno.zip to a folder "DiracGauginos" in the SPheno root directory and compile with

   <code>make Model=DiracGauginos</code>

This produces the library <code>./lib/libSPhenoDiracGauginos.a</code> and the executable <code>./bin/SPhenoDiracGauginos</code>. (Tested with SPheno-4.0.3)

Note that compared to the version exported from SARAH, we have changed <code>InputOutput.f90</code> to have an SLHA1-like ordering of the squarks, so that, for instance, PDG 1000006 is always the lighter stop.

The two additional scripts provided serve to add the QNUMBERS blocks and change "DECAY1L" to "DECAY" in the SLHA files. This is necessary for event generation with MadGraph5 / Pythia.
description: {"references": ["Guillaume Chalons, Mark D. Goodsell, Sabine Kraml, Humberto Reyes-Gonz\u00e1lez, Sophie L. Williamson, arXiv:1812.09293 [hep-ph]"]}
DOI: 10.5281/zenodo.6066153
title: Mothra training data

description: Training data related to the mothra project.
DOI: 10.6084/m9.figshare.14869509
title: Table S1_Characteristics of selected studies.docx

description: Table S1 - Characteristics of selected studies. Supplementary data for Ziam, Gignac, Mc Sween-Cadieux & Courant (2021) published in Evidence & Policy<br>
DOI: 10.6084/m9.figshare.20043665
title: AQME paper examples

description: This dataset contains example for our newly developed program AQME : Automated Quantum Mechnical Envirnments for chemical analysis. We show the utility of independent modules in multiple end-to-end examples covering both jupyter notebook and HPC computing.
DOI: 10.25452/figshare.plus.19629864
title: RNA-seq Titration Results supporting "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously"

description: This data accompanies the manuscript "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously" by Foltz, Taroni, and Greene https://doi.org/10.1038/s42003-023-04588-6 Please refer to our github page. The file contains all the raw input data, output files needed for plotting, and the intermediate files (including models and normalized data) from one repeat (seed 3274). <br> Abstract: Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, the majority of available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
DOI: 10.5067/gpm/imergdf/day/05
title: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V05
DOI: 10.5281/zenodo.3384388
title: MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

description: This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2]. This dataset is made available by Hitachi, Ltd. under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. A baseline sample code for anomaly detection is available on GitHub: https://github.com/MIMII-hitachi/mimii_baseline/ *1: This version "public 1.0" contains four models (model ID 00, 02, 04, and 06). The rest three models will be released in a future edition. [1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019. [2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.
description: {"references": ["Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, \"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,\" arXiv preprint arXiv:1909.09347, 2019.", "Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, \"MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,\" in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019."]}
DOI: 10.5281/zenodo.7503482
title: Data supplement for "Nonreciprocity induces resonances in two-field Cahn-Hilliard model".

description: Mathematica notebooks that support the weakly nonlinear analysis and the codes that create all figures in "Nonreciprocity induces resonances in two-field Cahn-Hilliard model". ".
DOI: 10.6084/m9.figshare.19248266
title: Dataset: Functional thresholds alter the relationship of plant resistance and recovery to drought

description: Data and Code from<br> "Ingrisch J, Umlauf N, Bahn M, Functional thresholds alter the relationship of plant resistance and recovery to drought" <br> <br> Data and Code are organised as an R-Project. Code is supplied as .r and .rmd files. In case of .rmd (r-markdown) script output is also supplied as .html.<br> <br> ./Data/Treatment_Info.txt: treatment information to each mesocosm (plot), Plant (Dac = Dactylis, Pla = Plantago), Rep (running number) and Water = target water content (in % of field capacity).<br> ./Data/01_compiled/: contains raw data used for analysis<br> ./Data/02_pro/: contains output from threshold regression analysis used for subsequent analysis<br> ./Data/03_output/: contains model output used in the manuscript<br> <br> ./Figs/ contains figures produced in analysis and used in manuscript<br> <br> ./R-Scripts/<br> /01_ThresholdRegression_ModelCalculation.r: calculates threshold regression models for drought and recovery data and multiple paramters, stores output<br> /02_ThresholdIdentification.rmd: uses model output from 01..., selects best models and creates figure<br> /03_GAMs.r: calculates response surface for productivity measures and bivariate response scheme.<br> 03_LWC-Responses.rmd: produces figure showing response in relation to relative leaf water deficit measured at peak drought.<br> 03_Microclimate.r: creates figure of microclimatic conditions during experiment.<br> <br>
DOI: 10.6084/m9.figshare.21984179.v2
title: Root traits of perennial C4 grasses contribute to cultivar variations in soil chemistry and species patterns in particulate and mineral-associated carbon pool formation

description: <strong>Abstract:</strong> Recent studies have indicated that the C4 perennial bioenergy crops switchgrass (<em>Panicum virgatum</em>) and big bluestem <em>(Andropogon gerardii</em>) accumulate significant amounts of soil carbon (C) owing to their extensive root systems. Soil C accumulation rates under these grasses are likely driven by inter- and intra-specific variability in plant traits. However, the mechanisms that underpin this variability in soil C storage remain unresolved. In this study we evaluated how inter- and intra-specific variation in root traits of cultivars from switchgrass (Cave-in-Rock, Kanlow, Southlow) and big bluestem (Bonanza, Southlow, Suther) affected the associations of soil C accumulation across soil fractions using stable isotope techniques. Our experimental field site was established in June 2008 at Fermilab in Batavia, IL. In 2018, soil cores were collected (30 cm depth; 4.8 cm diameter) from the root zone of all cultivars. We measured root biomass, root diameter, specific root length, bulk soil C and C associated with coarse and fine particulate organic matter (CPOM, FPOM) plus silt- and clay-sized fractions. Cultivar monocultures of both C4 species were established on soils that supported C3 grassland for 36 years before planting, which allowed us to use differences in the natural abundance of stable C isotopes to quantify C4 plant-derived C. We also characterized organic matter chemical class composition in root-zone soil using high resolution FTICR mass spectrometry. We found that species accumulated C through different mechanisms. Big bluestem cultivars had larger root systems that increased C4 plant-derived C in the POM-C pool, while switchgrass cultivars increased the C4 plant-derived C in the clay fraction via differences in root morphology and soil chemistry. This highlights the importance of both POM-C and mineral associated C in building soil carbon pools. <br> <br> <strong>Abbreviations:</strong> <strong>Species:</strong> BB = Big Bluestem SG = Switchgrass <strong>Cultivars:</strong> BO = Bonanza BSL = BB Southlow ST = Suther CR = Cave-in-Rock KA = Kanlow SSL = SG Southlow <strong>Depth:</strong> A = 0-10 cm B = 10-20 cm C = 20-30 cm
DOI: 10.5281/zenodo.7042090
title: simon-lledo/Ecology_deep sea mass crustacean falls_rawObservations

description: Initial release following acceptance at <em>Ecology</em> of Simon-Lledó et al. <strong>Mass falls of crustacean carcasses link surface waters and the deep seafloor. </strong> Data (.csv) contains: Location (Latitude/Longitude), Seascape type, Transect code, and Standard Carapace Length (SCL, mm) of the crustacean carcasses assessed to investigate the (abyssal) mass fall presented in the study.
DOI: 10.6073/pasta/1dbadeff81bf3e01ce4b188ffc93123f
title: PIE LTER Year 2015, 15 minute measurements of conductivity, water temperature at the Parker River head of tide, Central St. Dam in Newbury, MA.

description: Year 2015, continuous measurements, every 15 minutes were made of conductivity, water temperature in the Parker River behind the head of tide Central St. Dam in Newbury, MA.
DOI: 10.6084/m9.figshare.22704439.v1
title: Kennedy - GAO - Data (Excel)

description: These are the data used to evaluate the prevalence of GAO reports by Congress and by agency. It also includes a random sample of reports with some descriptive data.
DOI: 10.6084/m9.figshare.22704439
title: Kennedy - GAO - Data (Excel)

description: These are the data used to evaluate the prevalence of GAO reports by Congress and by agency. It also includes a random sample of reports with some descriptive data.
DOI: 10.5281/zenodo.5507189
title: Malawi Active Fault Database

description: First release of the Malawi Active Fault Database (MAFD) associated with a publication in review with the journal Geochemistry, Geophysics and Geosystems. To reference this database please refer to the latest release of the dataset on Github and Zenodo and please also cite: Williams, J. N., Wedmore, L. N. J., Scholz, C. A., Kolawole, F., Wright, L. J. M., Shillington, D., Fagereng, Å., Biggs, J., Mdala, H., Dulanya, Z., Mphepo, F., Chindandali, P. R. N., Werner, M. J. (2022), The Malawi Active Fault Database: an onshore-offshore database for regional assessment of seismic hazard and tectonic evoultion. _Geochemistry, Geophysics, Geosystems_, 23(5), e2022GC010425. https://doi.org/10.1029/2022GC010425 For full details of the database, please refer to the journal article above.
DOI: 10.5281/zenodo.6355474
title: Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021

description: <strong>Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup>. <sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland <sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland For further information, please contact: tom.muller.1@unil.ch <strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ <strong>Data Description</strong> 9 piezometers consisting of fully screened plastic tubes were installed at an averaged depth of 1.5 to 2m in the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). They cover four transects perpendicular to the stream from downstream (A) to upstream to (D). Water table elevation and temperature were recorded in each well at a 10 minute interval using SparkFun MS5803-14BA pressure sensors. Sensor resolution is 1 mm and 0.01 °C, sensor accuracy is ± 2 cm and ± 0.8°C. Sensor bias was verified and corrected by bi-monthly manual groundwater stage measurements. Water temperature was not manually corrected and may be subject to some unidentified bias. Piezometer location can be visualized in<strong><em> overview_piezo.jpg</em> </strong> Piezometer coordinates are available in<strong> </strong>shapefile <strong><em>GPS_piezometers.zip</em></strong> (coordinate system: swiss LV95 (EPSG:2056)) Piezometers name matches the labelling used in Müller et al. 2022 (A1,A2 to D1,D2). Two additionnal piezometers (BinjUp & BinjDown) used for a specific salt tracing analysis (see <em>ERT_timelapse_salt_tracer.gif </em>at https://zenodo.org/record/6342767#.YjBRmTXjJlh) are also available. Finally an additional piezometer (B1-2) located between B1 and B2 is also available although it was not used in Müller et al. 2022. <strong>Data Structure</strong> <strong>df_piezometers.csv</strong> is formated as a tidy dataframe with 10 minute interval and following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> w_elevation: Water table elevation [m. asl]<br> w_temperature : Groundwater temperature [°C]<br> - <em>name </em>: name of piezometer (see coordinates in GPS_piezometers.zip)<br> - <em>dateUTC </em>: date with UTC timezone Data can be quickly vizualized in<strong> plot_piezo.png</strong> or interactively in a browser using <strong>plot_piezo_interactive.html</strong>
DOI: 10.5281/zenodo.7599914
title: Dataset for the climate-related financial policy index (CRFPI)

description: Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.
DOI: 10.18462/iir.iccc.2020.292878
title: Improving Cold Chain Energy Efficiency: EU H2020 project for facilitating energy efficiency improvements in SMEs of the food and beverage cold chains.
DOI: 10.18750/lengthchange.2018.r2018
title: Swiss Glacier Length Change (release 2018)

description: Glacier length changes are measured annually at the tongues of all larger and on many of the smaller Swiss glaciers and provide an overview of their long-term variations. The dataset contains all available observations up to present and is delivered as a .csv file.
DOI: 10.5066/p96nii4b
title: Brown trout movement data in Glen and Grand Canyons, Arizona, USA

description: These data were compiled to test hypotheses regarding drivers of movement of brown trout. Objectives of our study were to test whether the degree of movement varied in response to placement of a weir in Bright Angel Creek, fall timed flooding events, or simply seasonal changes. These data represent summarized capture histories of brown trout in terms of states based on physical locations, data on removal efforts in Bright Angel Creek, and summaries of effort in the mainstem Colorado River. These data were collected at several locations along the Colorado River in Glen and Grand Canyon, including Bright Angel Creek from 2011 to 2018. These data were collected by U.S. Geological Survey, National Park Service, and Arizona Game and Fish. These data can be used to test hypotheses regarding drivers of brown trout movement in the Colorado River in its Grand Canyon segment.
DOI: 10.5067/7gfqko1t43rw
title: MERRA-2 tavg3_3d_rad_Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Radiation Diagnostics V5.12.4
DOI: 10.5281/zenodo.7133506
title: Reference data set for a Norwegian medium voltage power distribution system

description: This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts:<br> 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points.<br> 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future.<br> 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system.
description: This work is funded by CINELDI - Centre for Intelligent Electricity Distribution, an 8-year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20). The authors gratefully acknowledge the financial support from the Research Council of Norway and the CINELDI partners.
DOI: 10.5281/zenodo.7607397
title: Rainfall data from WRF simulations for the Atacama Desert for present and mid-Pliocene climate

description: We provide model output for rainfall from WRF experiments for the present-day and mid-Pliocene climate. These are netCDF files that contain processed data shown in figures of Reyers et al. (accepted). Details on the files and content are listed in the primary data information Reyers_et_al_primary_data_information.pdf Refer to Reyers et al. (2022) for the full information on the data production and interpretation. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1198. The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 268236062 – SFB1211 "Earth-evolution at the dry limit" (https://sfb1211.uni-koeln.de/). <strong>Reference</strong> Reyers, M., Fiedler, S., Ludwig, P., Böhm, C., Wennrich, V., and Shao, Y.: On the importance of moisture conveyor belts from the tropical East Pacific for wetter conditions in the Atacama Desert during the Mid-Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-72, 2022, accepted.
description: {"references": ["https://doi.org/10.5194/cp-2022-72"]}
DOI: 10.5445/ir/1000156063
title: Research data for ”Microphysical Pathways Active within Thunderstorms and Their Sensitivity to CCN Concentration and Wind Shear”
DOI: 10.6084/m9.figshare.14419262.v1
title: Instrument validity, sampling and result of implementation discovery learning in online setting

description: The data contains item validity, reliability, discrepancy and difficulty level of the item questions; sample selection; data on learning outcomes and student independence, including the calculation of the t-test.
DOI: 10.6084/m9.figshare.20489916
title: A plastid phylogenomic framework for the palm family (Arecaceae)

description: Sequence matrices and phylogenetic trees of the plams.
DOI: 10.15139/s3/zyvhuo
title: Cohort Level Dataset: Observational Studies of Adults with Accelerometry Measured Physical Activity and Sedentary Behavior through June 1, 2021

description: We conducted a systematic scoping review to identify observational studies of adults that collected accelerometry assessed physical activity or sedentary behavior from studies published through June 1, 2021. This cohort level dataset provides a listing of each study identified from the search. A separate accelerometer level dataset is available. The two datasets can be linked by the variable IDCOHORT.
DOI: 10.17632/v9y9drdxbf.1
title: Chen_Orton_WRR_DATA

description: Model inputs (2), bathymetric data (1) and selected model results (3) are available in the following repository:
Chen, Z., & Orton, P. M. (2022). Effects of Storm Surge Barrier Closures on Estuary Saltwater Intrusion and Stratification. (submitted) Water Resources Research.