cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3711626
title: Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"

description: The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (ReTiSAR) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication: <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> Directory "SMA sampling grids": Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1) Shell script "record_snr.sh": Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations All captured signals are contained in the "SNR" directory Matlab script "calculate_snr.m": Visualize the raw captured input and output signals (like Figure 1 for all configurations) Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations) Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting) All generated plots are contained in the "SNR" directory Shell script "record_noise.sh": Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix) All captured signals are contained in the "EM32 measurements" directory Pictures of the measurement setup are contained in the "Pictures" subdirectory Matlab script "calculate_EM32_noise_levels.m": Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2) Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations) Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)
description: {"references": ["H. Helmholz, D. Lou Alon, S. V. Amengual Gar\u00ed, and J. Ahrens, \"Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals,\" in Forum Acusticum, 2020, pp. 1349\u20131356, doi: 10.48465/fa.2020.0074."]}
DOI: 10.5281/zenodo.3711625
title: Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"

description: The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (ReTiSAR) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication: <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> Directory "SMA sampling grids": Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1) Shell script "record_snr.sh": Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations All captured signals are contained in the "SNR" directory Matlab script "calculate_snr.m": Visualize the raw captured input and output signals (like Figure 1 for all configurations) Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations) Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting) All generated plots are contained in the "SNR" directory Shell script "record_noise.sh": Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix) All captured signals are contained in the "EM32 measurements" directory Pictures of the measurement setup are contained in the "Pictures" subdirectory Matlab script "calculate_EM32_noise_levels.m": Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2) Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations) Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)
description: {"references": ["H. Helmholz, D. Lou Alon, S. V. Amengual Gar\u00ed, and J. Ahrens, \"Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals,\" in Forum Acusticum, 2020, pp. 1349\u20131356, doi: 10.48465/fa.2020.0074."]}
DOI: 10.34740/kaggle/dsv/6233196
title: 3D Synthetic ASL Digits
DOI: 10.5281/zenodo.8206597
title: nematoplana_nigrocapitula_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8206596
title: nematoplana_nigrocapitula_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23818869
title: Supplemental Table 1- Seizure and Seizure-Related HPO Terms

description: Supplemental Table 1 for the manuscript "Phenotypic overlap between rare disease patients and variant carriers in large population cohort informs biological mechanisms"
DOI: 10.6084/m9.figshare.23818869.v1
title: Supplemental Table 1- Seizure and Seizure-Related HPO Terms

description: Supplemental Table 1 for the manuscript "Phenotypic overlap between rare disease patients and variant carriers in large population cohort informs biological mechanisms"
DOI: 10.6084/m9.figshare.3761562.v2664
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.15482/usda.adc/1529363
title: Data from: Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination.

description: Pollen data from the manuscript titled: Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination, published in Environmental Entomology. Pollen was collected from honey bee (Apis mellifera) and bumble bee (Bombus impatiens) forager returning to the colony, while in commercial highbush blueberry fields in SW Michigan for crop pollination. Pollen was identified visually using morphological features.
Funding also provided by Project GREEEN award GR18-038. Project GREEEN, Michigan’s plant agriculture initiative housed at Michigan State University, is a cooperative effort by plant-based commodity groups and businesses in cooperation with Michigan State University AgBioResearch, Michigan State University Extension and the Michigan Department of Agriculture and Rural Development to advance Michigan’s economy through plant-based agriculture.
DOI: 10.5281/zenodo.8206603
title: HISTA App rds file needed for running locally

description: put in inst/app/data folder of HISTA https://zenodo.org/record/8206586 https://github.com/eisascience/HISTA
DOI: 10.5281/zenodo.8206602
title: HISTA App rds file needed for running locally

description: put in inst/app/data folder of HISTA https://zenodo.org/record/8206586 https://github.com/eisascience/HISTA
DOI: 10.34740/kaggle/dsv/6233277
title: 3D Synthetic ASL Digits
DOI: 10.6084/m9.figshare.23586321
title: Children and Youth in India 2021: Yoga and MVPA

description: The "Children and Youth in India 2021" dataset includes valuable data on physical activity levels (MVPA), yoga practice, school infrastructure, and the presence of active friendships among children and youth.
DOI: 10.6084/m9.figshare.23586321.v2
title: Children and Youth in India 2021: Yoga and MVPA

description: The "Children and Youth in India 2021" dataset includes valuable data on physical activity levels (MVPA), yoga practice, school infrastructure, and the presence of active friendships among children and youth.
DOI: 10.6084/m9.figshare.23586321.v1
title: Children and Youth in India 2021: Yoga and MVPA

description: The "Children and Youth in India 2021" dataset includes valuable data on physical activity levels (MVPA), yoga practice, school infrastructure, and the presence of active friendships among children and youth.
DOI: 10.5281/zenodo.8196013
title: Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level

description: Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"
DOI: 10.5281/zenodo.8196012
title: Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level

description: Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"
DOI: 10.17632/dfgjkcbhgr.1
title: Replication Data for: Saving More in Groups: Field Experimental Evidence from Chile

description: The data and programs replicate the analysis from "Saving More in Groups: Field Experimental Evidence from Chile" by Felipe Kast, Stephan Meier, and Dina Pomeranz (2018).
DOI: 10.5281/zenodo.8206682
title: Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"

description: The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.
DOI: 10.5281/zenodo.8206681
title: Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"

description: The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.
DOI: 10.5281/zenodo.8206408
title: Matrix Profile of Seismological Data Repository

description: The Computed Matrix Profile (MP) repository for seismic data. Please see below the archived manuscript of Shakibay Senobari et al., (Submitted to JGR, under review) for more information regarding the Matrix Profile and data set: essopenarchive.org/604953/p1vlDMs-5b0zsrs7C033Ug https://doi.org/10.1002/essoar.10512525.1
DOI: 10.5281/zenodo.8206657
title: On the Effects of Program Slicing for Vulnerability Detection during Code Inspection

description: Dataset of the empirical experiment presented in the paper On the Effects of Program Slicing for Vulnerability Detection during Code Inspection. The dataset includes the participants' responses regarding their background, and the responses of the tasks from the experiment.
DOI: 10.5281/zenodo.8206656
title: On the Effects of Program Slicing for Vulnerability Detection during Code Inspection

description: Dataset of the empirical experiment presented in the paper On the Effects of Program Slicing for Vulnerability Detection during Code Inspection. The dataset includes the participants' responses regarding their background, and the responses of the tasks from the experiment.
DOI: 10.5281/zenodo.8206695
title: On the Effects of Program Slicing for Vulnerability Detection during Code Inspection

description: Dataset of the empirical experiment presented in the paper On the Effects of Program Slicing for Vulnerability Detection during Code Inspection. The dataset includes the participants' responses regarding their background, and the responses of the tasks from the experiment.
DOI: 10.25345/c5639kg23
title: MassIVE MSV000092567 - Stellaria alsine-Orbitrap MS-2023-laboratory of Pharmacognosy
title: To study of chemical diversity and identify potential therapeutic candidate for treating inflammatory diseases from Stellaria alsine
DOI: 10.5281/zenodo.8178792
title: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"

description: The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the build.sh script in the solver's sub-directory. The archive christen-et-al-ecai2023-data contains all the scripts necessary to run the experiments<br> presented in the paper, as well as all the raw and processed data. The archive christen-et-al-ecai2023-benchmarks contains all the benchmarks from the competition as well as our compiled PDDL and SAS+ versions, and the scripts used for the compilation. For a detailed explanation, see the README file within the archives. For licensing information about the solvers, consult the LICENSE file within the solvers archive.
DOI: 10.5281/zenodo.8178793
title: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"

description: The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the build.sh script in the solver's sub-directory. The archive christen-et-al-ecai2023-data contains all the scripts necessary to run the experiments<br> presented in the paper, as well as all the raw and processed data. For a detailed explanation, see the README file within the archives. For licensing information about the solvers, consult the LICENSE file within the solvers archive.
DOI: 10.5281/zenodo.8206715
title: Code and experimental data for the ECAI 2023 paper "PARIS: Planning Algorithms for Reconfiguring Independent Sets"

description: The archive chirsten-et-al-ecai2023-solvers contains the code necessary to generate the singularity images used in the experimental evaluation, except for CPLEX which is needed for some PARIS images. The solvers can be built using the build.sh script in the solver's sub-directory. The archive christen-et-al-ecai2023-data contains all the scripts necessary to run the experiments<br> presented in the paper, as well as all the raw and processed data. The archive christen-et-al-ecai2023-benchmarks contains all the benchmarks from the competition as well as our compiled PDDL and SAS+ versions, and the scripts used for the compilation. For a detailed explanation, see the README file within the archives. For licensing information about the solvers, consult the LICENSE file within the solvers archive.
DOI: 10.5281/zenodo.8206765
title: Tara Prokaryote Annotated 16s OTU Counts

description: "Tara Oceans systematically collected ~35,000 samples for morphological, genetic, and environmental analyses using standardized protocols across multiple depths at global scale, aiming to facilitate a holistic study on how environmental factors and biogeochemical cycles affect oceanic life." (1) "For each prokaryote-enriched sample (N=139), we extracted metagenomic merged Illumina reads (miTAGs) that contained signatures of the 16S rRNA gene (Logares et al. 2013). These fragments were mapped to a set of 16S reference sequences that were downloaded from the SILVA database (Release 115: Quast et al. 2013) and clustered into 97% operational taxonomic units. The OTU count table was summarized at different taxonomic levels." (2)<br> (1): https://www-science-org.offcampus.lib.washington.edu/doi/full/10.1126/science.1261359<br> (2): http://ocean-microbiome.embl.de/companion.html
DOI: 10.5281/zenodo.8206764
title: Tara Prokaryote Annotated 16s OTU Counts

description: "Tara Oceans systematically collected ~35,000 samples for morphological, genetic, and environmental analyses using standardized protocols across multiple depths at global scale, aiming to facilitate a holistic study on how environmental factors and biogeochemical cycles affect oceanic life." (1) "For each prokaryote-enriched sample (N=139), we extracted metagenomic merged Illumina reads (miTAGs) that contained signatures of the 16S rRNA gene (Logares et al. 2013). These fragments were mapped to a set of 16S reference sequences that were downloaded from the SILVA database (Release 115: Quast et al. 2013) and clustered into 97% operational taxonomic units. The OTU count table was summarized at different taxonomic levels." (2)<br> (1): https://www-science-org.offcampus.lib.washington.edu/doi/full/10.1126/science.1261359<br> (2): http://ocean-microbiome.embl.de/companion.html
DOI: 10.5281/zenodo.8206771
title: Swift/BAT-GUANO upper limits fits file for S230731an

description: Swift/BAT-GUANO upper limits fits file for S230731an
DOI: 10.5281/zenodo.8206770
title: Swift/BAT-GUANO upper limits fits file for S230731an

description: Swift/BAT-GUANO upper limits fits file for S230731an
DOI: 10.25901/j1b2-ae38
title: NSW Forest Monitoring and Improvement Program Post-Fire Debris Flow Mapping in the Tumut and Tuross Catchments
DOI: 10.5281/zenodo.8206795
title: serinus_canicollis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8206794
title: serinus_canicollis_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23819112
title: <b>Factors associated with appearance-based social comparison in college women: a cross-sectional study</b>

description: This is the dataset of our study, entitled "Factors associated with appearance-based social comparison in college women: a cross-sectional study". In this dataset, all the variables presented in the study can be found, being possible to reproduct our analyses and results.
DOI: 10.6084/m9.figshare.23819112.v1
title: <b>Factors associated with appearance-based social comparison in college women: a cross-sectional study</b>

description: This is the dataset of our study, entitled "Factors associated with appearance-based social comparison in college women: a cross-sectional study". In this dataset, all the variables presented in the study can be found, being possible to reproduct our analyses and results.
DOI: 10.5281/zenodo.8206809
title: spinus_pinus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8206808
title: spinus_pinus_zanfona_1.fasta.gz

description: .
DOI: 10.3886/e116250v1-140574
title: Replication data for: Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results

description: Applying the Coase Theorem to marital bargaining suggests that shifting from consent to unilateral divorce laws will not affect divorce rates. I show that existing evidence suggesting large effects of divorce laws on divorce rates reflect a failure to explicitly model the dynamic response of divorce rates to a shock to the legal regime. When accounting for these dynamics, I find that unilateral divorce spiked following the adoption of unilateral divorce laws, but that this rise largely reversed itself within a decade. Overall, these changes in family law explain very little of the rise in divorce over the past half-century. (JEL C78, J12)
DOI: 10.5281/zenodo.8206823
title: Integration of imaging modalities with lipidomic characterization to investigate single cell MSCs potency metrics

description: This dataset contains images used in the analysis for the publication mentioned above.
DOI: 10.5281/zenodo.8206822
title: Integration of imaging modalities with lipidomic characterization to investigate single cell MSCs potency metrics

description: This dataset contains images used in the analysis for the publication mentioned above.
DOI: 10.5519/qd.3581ud2e
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (822793 records)
DOI: 10.5281/zenodo.8206828
title: SG - July 2023

description: Measurements of SG (Global) in July 2023 Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206827
title: SG - July 2023

description: Measurements of SG (Global) in July 2023 Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206830
title: LPL1 - July 2023

description: Measurements of LPL1 (Teide NP) in July 2023 Tenerife Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206829
title: LPL1 - July 2023

description: Measurements of LPL1 (Teide NP) in July 2023 Tenerife Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206832
title: LPL2 - July 2023

description: Measurements of LPL2 (Caldera de Taburiente NP) in July 2023 La Palma Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206831
title: LPL2 - July 2023

description: Measurements of LPL2 (Caldera de Taburiente NP) in July 2023 La Palma Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206836
title: LPL3 - July 2023

description: Measurements of LPL3 (Madeira East Coast) in July 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206835
title: LPL3 - July 2023

description: Measurements of LPL3 (Madeira East Coast) in July 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206838
title: LPL4 - July 2023

description: Measurements of LPL4 (Ilha do Corvo) in July 2023 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206837
title: LPL4 - July 2023

description: Measurements of LPL4 (Ilha do Corvo) in July 2023 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206840
title: LPL5 - July 2023

description: Measurements of LPL5 (Tejeda) in July 2023 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206839
title: LPL5 - July 2023

description: Measurements of LPL5 (Tejeda) in July 2023 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206844
title: LPL7 - July 2023

description: Measurements of LPL7 (Archipielago Chinijo) in July 2023 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206843
title: LPL7 - July 2023

description: Measurements of LPL7 (Archipielago Chinijo) in July 2023 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206846
title: LPL8 - July 2023

description: Measurements of LPL8 (Timanfaya NP) in July 2023 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206845
title: LPL8 - July 2023

description: Measurements of LPL8 (Timanfaya NP) in July 2023 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206848
title: LPL11 - July 2023

description: Measurements of LPL11 (Frontera Rural Park) in July 2023 El Hierro Light Pollution Laboratorie info