cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6382496
title: Sticky Pi -- Machine Learning Data, Configuration and Models

description: <strong>Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/)</strong> Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier. <strong>Universal Insect Detector:</strong> `universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. `data/` – A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects `output/` `model_final.pth` – the model as trained for the publication `config/` `config.yaml `– The configuration file defining the hyperparameters to train the model `mask_rcnn_R_101_C4_3x.yaml` – the base configuration file from which config is derived <strong>Siamese Insect Matcher</strong> `siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. `data/` – a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `&lt;device&gt;.&lt;datetime_frame_1&gt;.&lt;datetime_frame_2&gt;.svg` `output/` `model_final.pth` – the model as trained for the publication `config/` `config.yaml` – The configuration file defining the hyperparameters to train <strong>Insect Tuboid Classifier:</strong> `insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication. `data/` `database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables. A directory tree of the form: `&lt;series_id&gt;/&lt;tuboid_id&gt;/`. Each terminal directory contains: `tuboid.jpg` – a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom – might be padded with empty images `metadata.txt` – a csv text file with columns: parrent_image_id – &lt;device&gt;.&lt;UTC_datetime&gt; X – the X coordinates of the object centroid Y – the Y coordinates of the object centroid scale – The scaling factor applied between the original and image and the 224 x 224 tile (&gt;1 =&gt; image was enlarged) `context.jpg` – a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes) `output/` `model_final.pth` – the model as trained for the publication config/ `config.yaml` – The configuration file defining the hyperparameters to train the model as well as the taxonomic labels
description: Second version. Added data to the UID and SIM. Minor changes in the configurations.
DOI: 10.5281/zenodo.4680118
title: Sticky Pi -- Machine Learning Data, Configuration and Models

description: <strong>Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/)</strong> Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier. <strong>Universal Insect Detector:</strong> `universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. `data/` – A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects `output/` `model_final.pth` – the model as trained for the publication `config/` `config.yaml `– The configuration file defining the hyperparameters to train the model `mask_rcnn_R_101_C4_3x.yaml` – the base configuration file from which config is derived <strong>Siamese Insect Matcher</strong> `siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. `data/` – a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `&lt;device&gt;.&lt;datetime_frame_1&gt;.&lt;datetime_frame_2&gt;.svg` `output/` `model_final.pth` – the model as trained for the publication `config/` `config.yaml` – The configuration file defining the hyperparameters to train <strong>Insect Tuboid Classifier:</strong> `insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication. `data/` `database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables. A directory tree of the form: `&lt;series_id&gt;/&lt;tuboid_id&gt;/`. Each terminal directory contains: `tuboid.jpg` – a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom – might be padded with empty images `metadata.txt` – a csv text file with columns: parrent_image_id – &lt;device&gt;.&lt;UTC_datetime&gt; X – the X coordinates of the object centroid Y – the Y coordinates of the object centroid scale – The scaling factor applied between the original and image and the 224 x 224 tile (&gt;1 =&gt; image was enlarged) `context.jpg` – a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes) `output/` `model_final.pth` – the model as trained for the publication config/ `config.yaml` – The configuration file defining the hyperparameters to train the model as well as the taxonomic labels
description: Second version. Added data to the UID and SIM. Minor changes in the configurations.
DOI: 10.6084/m9.figshare.19387538
title: Neural ADMIXTURE - datasets

description: Contains the datasets <i>All-Chms</i>, <i>Chm-22</i> and <i>Chm-22-Sim</i> used in the experiments of the article Neural ADMIXTURE: rapid population clustering with autoencoders. If you use this data, please cite the corresponding article.<br><br>Datasets are different compilations of publicly available data (<i>1000 Genomes Project</i>, <i>Simons Genome Diversity Project</i>, <i>Human Genome Diversity Project</i>). <i>Chm-22-Sim</i> also contains synthetic data generated from existing one using a tool which will be available to the public in the near future.<br><br>
DOI: 10.6084/m9.figshare.19410476
title: City climate and landscape structure shape pollinators, nectar and transported pollen along a gradient of urbanization

description: The datasets contain field and laboratory data used in the publication entitled "City climate and landscape structure shape pollinators, nectar and transported pollen along a gradient of urbanization" by Biella et al, Journal of Applied Ecology, 2022.<br>Pollinator abudance, nectar sugar and plant richness in pollen are given with site and coordinates.<br>Landscape and climate data can be obtained from public repositories as indicated in the Supporting Information, Appendix A1 of the publication.
DOI: 10.6084/m9.figshare.19410476.v1
title: City climate and landscape structure shape pollinators, nectar and transported pollen along a gradient of urbanization

description: The datasets contain field and laboratory data used in the publication entitled "City climate and landscape structure shape pollinators, nectar and transported pollen along a gradient of urbanization" by Biella et al, Journal of Applied Ecology, 2022.<br>Pollinator abudance, nectar sugar and plant richness in pollen are given with site and coordinates.<br>Landscape and climate data can be obtained from public repositories as indicated in the Supporting Information, Appendix A1 of the publication.
DOI: 10.5281/zenodo.6379166
title: Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

description: Here are the datasets for the publication named "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation" published in Nature Communications. Experimental cycling data for three commercial 18650 type batteries (Dataset_1:NCA battery, Dataset_2:NCM battery, and Dataset_3:NCM+NCA_battery) are given, where each csv file corresponds to one cell cycling data. The cells are named as CY<em>X-Y_Z</em>-#<em>N</em> according to their cycling conditions. <em>X</em> means the temperature, <em>Y_Z</em> represents the charge_discharge current rate, #<em>N</em> is the cell tag. Each csv file has 9 columns, including cycle time ('time/s'), controlled voltage and current ('control/V/mA'), battery voltage ('Ecell/V'), applied current ('&lt;I&gt;/mA'), charge or discharge electricity ('Q discharge/mA.h' and 'Q discharge/mA.h'), controlled voltage or current ('control/V', 'control/mA' and ), and cycle number ('cycle number'). In the impedance data, one representative cell from each cycling condition is chosen for the discussion in the main text. More detailed descriptions can be found in the zip file.
DOI: 10.5281/zenodo.6382405
title: Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis

description: Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882 Changelog: V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.
DOI: 10.5281/zenodo.5720723
title: Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis

description: Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882 Changelog: V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.
DOI: 10.5281/zenodo.5503876
title: H-MAGMA Protocol

description: This folder contains the full protocol needed to generate the variant-gene annotation file to run Hi-C Coupled MAGMA or H-MAGMA. In addition to the codes, we have included variant-gene annotation files for 28 tissue and cell types from Jung et al. 2019. We have updated portions of the code from our previous Zenodo upload under the DOI https://doi.org/10.5281/zenodo.5503877
DOI: 10.5281/zenodo.6158206
title: Replication Package for the Paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community"

description: This is the replication package for the paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community", including dataset and so on (see the description below) : <strong>Data of Code Snippets in Code Review.xlsx</strong> is the dataset of our paper, which contains 10,790 review comments collected from the Nova project and Neutron project of OpenStack community. Among all the review comments, 626 review comments contain code snippets. For the rows of review comments with code snippets, we filled them with blue color as an indicator. <strong>Examples for Each Purpose.xlsx</strong> contains six review comment examples for the six detailed purposes mentioned in our paper (see Section 4.2). <strong>README.md</strong>
DOI: 10.5281/zenodo.6382399
title: Buteo albonotatus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6382398
title: Buteo albonotatus SPAdes preassembly

description: .
DOI: 10.7284/141108
title: Multiplexed data collected during research cruise AE2005 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141109
title: Multiplexed data collected during research cruise AE2005 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141110
title: Multiplexed data collected during research cruise AE2005 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141112
title: Multiplexed data collected during research cruise AE2005 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141107
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE2005 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141134
title: Multiplexed data collected during research cruise AE2008 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141135
title: Multiplexed data collected during research cruise AE2008 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141136
title: Multiplexed data collected during research cruise AE2008 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141138
title: Multiplexed data collected during research cruise AE2008 using a BIOS METOC instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141132
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE2008 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141133
title: Singlebeam Echosounder data collected during research cruise AE2008 using a Knudsen 3260 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.6350431
title: Data: Physical constraints on thermoregulation and flight drive morphological evolution in bats

description: Body size and shape fundamentally determine organismal energy requirements by modulating heat and mass exchange with the environment and the costs of locomotion, thermoregulation, and maintenance. Ecologists have long used the physical linkage between morphology and energy balance to explain why the body size and shape of many organisms vary across climatic gradients, e.g., why larger endotherms are more common in colder regions. However, few modeling exercises have aimed at investigating this link from first principles. Body size evolution in bats contrasts with the patterns observed in other endotherms, probably because physical constraints on flight limit morphological adaptations. Here, we develop a biophysical model based on heat transfer and aerodynamic principles to investigate energy constraints on morphological evolution in bats. Our biophysical model predicts that the energy costs of thermoregulation and flight, respectively, impose upper and lower limits on the relationship of wing surface area to body mass (S-MR), giving rise to an optimal S-MR at which both energy costs are minimized. A comparative analysis of 278 species of bats supports the model’s prediction that S-MR evolves toward an optimal shape and that the strength of selection is higher among species experiencing greater energy demands for thermoregulation in cold climates. Our study suggests that energy costs modulate the mode of morphological evolution in bats—hence shedding light on a long-standing debate over bats’ conformity to ecogeographical patterns observed in other mammals—and offers a procedure for investigating complex macroecological patterns from first principles.
DOI: 10.5281/zenodo.6350430
title: Data: Physical constraints on thermoregulation and flight drive morphological evolution in bats

description: Body size and shape fundamentally determine organismal energy requirements by modulating heat and mass exchange with the environment and the costs of locomotion, thermoregulation, and maintenance. Ecologists have long used the physical linkage between morphology and energy balance to explain why the body size and shape of many organisms vary across climatic gradients, e.g., why larger endotherms are more common in colder regions. However, few modeling exercises have aimed at investigating this link from first principles. Body size evolution in bats contrasts with the patterns observed in other endotherms, probably because physical constraints on flight limit morphological adaptations. Here, we develop a biophysical model based on heat transfer and aerodynamic principles to investigate energy constraints on morphological evolution in bats. Our biophysical model predicts that the energy costs of thermoregulation and flight, respectively, impose upper and lower limits on the relationship of wing surface area to body mass (S-MR), giving rise to an optimal S-MR at which both energy costs are minimized. A comparative analysis of 278 species of bats supports the model’s prediction that S-MR evolves toward an optimal shape and that the strength of selection is higher among species experiencing greater energy demands for thermoregulation in cold climates. Our study suggests that energy costs modulate the mode of morphological evolution in bats—hence shedding light on a long-standing debate over bats’ conformity to ecogeographical patterns observed in other mammals—and offers a procedure for investigating complex macroecological patterns from first principles.
DOI: 10.6084/m9.figshare.19410956
title: SRR3414629.uniq.sorted.bam

description: sorted bam files
DOI: 10.6084/m9.figshare.19410956.v2
title: SRR3414629.uniq.sorted.bam

description: sorted bam files
DOI: 10.6084/m9.figshare.19410956.v1
title: SRR3414629.uniq.sorted.bam

description: sorted bam files
DOI: 10.6084/m9.figshare.19410233
title: Areas of application of cryoblasting and Consumer requirements for the design of cryoblasters

description: Areas of application of cryoblasting
DOI: 10.6084/m9.figshare.19410233.v2
title: Areas of application of cryoblasting and Consumer requirements for the design of cryoblasters

description: Areas of application of cryoblasting
DOI: 10.6084/m9.figshare.19410233.v1
title: Areas of application of cryoblasting

description: Areas of application of cryoblasting
DOI: 10.5281/zenodo.6373554
title: FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data

description: Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI).
description: This work was funded by Swiss National Foundation grant 320030_149586/1 to DRB and Wellcome Centre for Human Neuroimaging (University College London, UK) core funding 203147/Z/16/Z. DRB and CCR are supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreements No. ERC-2018 CoG-816564 ActionContraThreat to DRB; ERC-2016 CoG-725355 BRAINCODES to CCR). DRB receives support from the National Institute for Health Research (NIHR) UCLH Biomedical Research Centre. CCR receives support from the Swiss National Science Foundation (100019L_173248).