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AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots
<p>This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, <strong>images.zip</strong>, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process.</p> <p>The second archive, <strong>videos_app_HR.zip</strong>, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization.</p> <p>The third archive, <strong>videos_app_TM.zip</strong>, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers.</p> <p>In addition to the visual data, the dataset includes a comma-separated values (CSV) file named <strong>information_all_trees.csv</strong>, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations.</p> <p>The column headers in <strong>information_all_trees.csv</strong> are as follows:</p> <ul> <li><strong>PLOT_ID</strong>: The numeric identifier for each sample plot.</li> <li><strong>tree_species_short</strong>: Abbreviation of the tree species.</li> <li><strong>caliper_dbh</strong>: The manually measured dbh of the tree in centimeters.</li> <li><strong>AVP_App1_dbh</strong>: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li><strong>AVP_App2_dbh</strong>: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li><strong>res_App1</strong>: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>res_App2</strong>: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>tree_species</strong>: The Latin name of the tree species, with genus and species connected by an "_".</li> <li><strong>tree_class</strong>: Classification of the tree into a species-specific category.</li> <li><strong>date</strong>: The date of the recordings.</li> <li><strong>time_App_1_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li><strong>time_App_2_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li><strong>time_manual_caliper_min</strong>: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li><strong>measuring_person</strong>: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li><strong>mean_slope_degrees</strong>: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> <p>This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
COgnitive intervention to Restore attention using nature Environment (CORE) study data
<p>The COgnitive intervention to Restore attention using nature Environment (CORE) study is single-blinded, two-group randomized-controlled pilot trial among patients with heart failure. The aims to test the preliminary efficacy of the newly developed Nature-VR, a virtual reality-based cognitive intervention that is based on the restorative effects of nature. The Nature-VR intervention group viewed 3-dimensional nature pictures using a virtual reality headset for 10 minutes per day, 5 days per week for 4 weeks (a total of 200 minutes). The active comparison group, Urban-VR, viewed 3-dimensional urban pictures using a virtual reality headset to match the Nature-VR intervention in intervention dose and delivery mode, but not in content. In this study, 73 participants with heart failure completed the baseline and randomized to either Nature-VR or Urban-VR. The target outcomes were attention, self-care of heart failure, and health-related quality of life (HRQoL). After baseline data collection, 4 follow-up data were collected at 4, 8, 26, and 52 weeks.</p>
Global Coastal Transect System (GCTS)
<p>Cross-shore coastal transects are essential to coastal monitoring, offering a consistent reference line to measure coastal change, while providing a robust foundation to map coastal characteristics and derive coastal statistics thereof. The Global Coastal Transect System consists of more than 11 million cross-shore coastal transects uniformly spaced at 100-m intervals alongshore, for all OpenStreetMap coastlines that are longer than 5 kilometers.</p> <p>While the data is available here for download, we highly recommend direct access via the cloud. For latest usage instructions please see the tutorials at https://github.com/TUDelft-CITG/coastpy. The dataset is extensively described in Calkoen, F. R., Luijendijk, A. P., Vos, K., Kras, E., & Baart, F. (2025). Enabling coastal analytics at planetary scale. <em>Environmental Modelling & Software</em>, <em>183</em>, 106257; please cite this paper when the data is used. </p>
Satellite-to-Ground QKD SKR dataset for P&M and Entanglement-based Protocols
<p>This dataset provides calculated Key Performance Indicators (KPIs) for satellite-to-ground quantum key distribution (QKD) links, modeled across Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO). The LEO orbit is modeled over a two-week period, while MEO and GEO orbits are modeled over a single day, whereas samples are provided with a sampling rate of 10 seconds.</p> <p>The satellite downlink channel is simulated using two QKD protocols: the Prepare-and-Measure protocol (Decoy-BB84) and the Entanglement-based protocol (BBM92). System specifications align with the LaiQa project source prototype, incorporating SNSPDs as the detection technology and assuming telescope-to-fiber coupling for ground reception.</p> <p>This dataset includes essential input metrics such as elevation angles for different orbits and various Optical Ground Stations (OGS) over time, along with key output metrics, including Secure Key Rates (SKR), Quantum Bit Error Rate (QBER), and Link Loss. Additionally, a comprehensive PDF guide is provided to assist with data handling and interpretation.</p> <p>Note: The results that are presented in the READ_ME file provide the volume of distilled keys and the number of distilled AES 256 keys over a time period of two weeks for different satellite orbits.</p>
Bidirectional and Unidirectional Charging Profiles of Electric Vehicles
<p>This dataset contains bidirectional and unidirectional charging profiles of Electric Vehicles (EVs) measured in laboratory environment at the Smart Grid Technology Lab of ie³ institute at TU Dortmund University. The dataset not only considers charging power and current but also harmonics/interharmonics emission of EV charging in both static and dynamic scenarios. Thus, it provides a solid foundation for the development of advanced EV charging algorithms and model validation. Raw data are available in csv format from the file <em>dataset_raw.zip</em> and a selection of merged measurements is provided in the file <em>dataset_merged.zip</em>.</p> <p>The following commercially available EV models are considered:</p> <ul> <li>Opel Corsa-e (2020)</li> <li>Fiat 500e (2022)</li> <li>Honda-e Advance (bidirectional, 2020)</li> <li>Nissan Leaf (bidirectional, 2020)</li> <li>VW ID.4 (2020)</li> <li>Hyundai Ioniq 5 (2021)</li> <li>Mitsubishi Eclipse Cross PHEV (bidirectional, 2022)</li> <li>Tesla Model Y SR (2022)</li> </ul> <p>The dataset is part of the deliverable D8.1 of DriVe2X project and is accompanied by a report including a description about data acquisition and measurement setup. The report is available from the project website's resources section. A more in-depth description of the tests and exemplary analysis is currently being prepared for publication.</p> <p><strong>References</strong></p> <ul> <li>DriVe2X project website: <a href="https://drive2x.eu/">Link</a></li> <li>CORDIS website: <a href="https://cordis.europa.eu/project/id/101056934">Link</a></li> <li>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></li> <li>Smart Grid Technology Lab: <a href="http://sgtl.et.tu-dortmund.de/">Link</a></li> </ul>
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
Invertebrates from the ANTARXXVII Leg1 expedition to the Bransfield Strait, Antarctica - images
<p>This upload contains photographs of Arthropoda, Nemertea, Mollusca, Annelida, Echinodermata and Nematoda samples from Admiralty bay, Bransfield Strait and Maxwell Bay taken by Louraine Salabao and Jolien Claes during the first leg of the ANTARXXVII campaign in the Southern Ocean aboard BAP Carrasco from December 24, 2019 to January 25, 2020.</p> <p>The occurrence dataset is available at https://ipt.biodiversity.aq/resource?r=antarxxvii-leg1, published by SCAR-AntOBIS under the license CC-BY 4.0. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via data-biodiversity-aq@naturalsciences.be.</p> <p>This dataset is part of the Refugia and Ecosystem Tolerance in the Southern Ocean (RECTO) project funded by Belgium Science Policy (BELSPO).</p>
Dataset from: "Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention"
<p>Dataset for Henare, D. T., Kadel, H., & Schubö, A. (2020). Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention. <em>Journal of Cognitive Neuroscience</em>, <em>32</em>(11), 2159-2177. <a href="https://doi.org/10.1162/jocn_a_01609">https://doi.org/10.1162/jocn_a_01609</a></p>
Aquatic Mollusca (Gastropoda and Bivalvia) from the Malaysian Borneo: A bibliographic records
<p>This data set contains the available aquatic Mollusca reported from Malaysian Borneo (East Malaysia) comprised of two provinces namely Sarawak and Sabah along with the federal territory of Labuan. </p>
Dataset for: Stereorandomization as a Method to Probe Peptide Bioactivity
<p>The upload contains additional primary data associated with the publication, including raw data in the original file format whenever possible.</p> <p>Data content: HRMS, HPLC-MS, CD, MD, TEM, Serum stability, Vesicle leakage assay, Cytotoxicity, Hemolysis.</p>
UniToBrain Dataset
<p>The University of Turin (UniTO) released the open-access dataset UniTOBrain collected for the homonymous Use Case 3 in the DeepHealth project (<a href="https://deephealth-project.eu/">https://deephealth-project.eu/</a>). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP). The dataset includes 100 training subjects and 15 testing subjects used in a submitted publication for the training and the testing of a Convolutional Neural Network (CNN, see for details: <a href="https://arxiv.org/abs/2101.05992">https://arxiv.org/abs/2101.05992</a>, <a href="https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model">https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model</a>, <a href="https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1">https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1</a>). The UniTO team released this dataset publicly. This is a subsample of a greater dataset of 258 subjects that will be soon available for download at <a href="https://ieee-dataport.org/">https://ieee-dataport.org/</a>.<br> CTP data from 258 consecutive patients were retrospectively obtained from the hospital PACS of Città della Salute e della Scienza di Torino (Molinette). CTP acquisition parameters were as follows: Scanner GE, 64 slices, 80 kV, 150 mAs, 44.5 sec duration, 89 volumes (40 mm axial coverage), injection of 40 ml of Iodine contrast agent (300 mg/ml) at 4 ml/s speed.</p> <p>Along with the dataset, we provide some utility files.</p> <p>dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition.</p> <p>dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models.</p> <p>dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit <a href="https://github.com/EIDOSlab/UC3-UNITOBrain">https://github.com/EIDOSlab/UC3-UNITOBrain</a> to have a full companion code where a U-Net model is trained over the dataset.</p> <p>As for UniToBrain Data and Metadata in machine-readable format see <a href="https://openview.metadatacenter.org/templates/https:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2">https://openview.metadatacenter.org/templates/https:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2</a>.</p>
Prosopographical Database of Judeans in Babylonia (outside Yahudu and the Murašû Archive)
<p>This is a prosopographical database of Judean persons in Babylonia outside the Yahudu corpus and the Murašû archive. It relates to Tero Alstola, 2020, <em>Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE</em> (Culture and History of the Ancient Near East 109. Leiden: Brill). For further information, see the readme file.</p>
Input files for simulation of potassium channels using the AMOEBA polarizable force field
<p>This dataset contains input Tinker xyz and key files for the simulation of KcsA potassium channels in DOPC bilayer, a simple script for converting CHARMM pdb file to Tinker xyz file, and modified Tinker source code to support one-dimensional position restraints.<br> "params.tar.gz" contains a description of the force field modifications.<br> <br> To use "mod2", add the following lines to the key file.</p> <pre><code>#compatible with amoebabio18.prm polarize 5 1.4500 0.3900 3 polarize 11 1.4500 0.3900 9 polarize 3 1.7500 0.3900 1 5 7 50 225 227 polarize 9 1.7500 0.3900 1 7 11 50 225 227</code></pre> <p> </p>
Investigating the conformal behaviour of SU(2) with one adjoint Dirac flavor --- data release
<p>This dataset collects data and analysis results for non-perturbative lattice field theory calculations investigating the SU(2) gauge theory with one Dirac fermion in the adjoint representation. More detailed information is included in the file README_datapackage.md</p>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
T2-weighted Kidney MRI Segmentation
<p>A dataset containing 100 T<sub>2</sub>-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p> <p>For more information about the dataset please refer to <a href="https://doi.org/10.1002/mrm.28768">this article.</a></p> <p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to <a href="https://github.com/alexdaniel654/Renal_Segmentor">this software.</a></p>
Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer
<p>The fourth dataset dedicated to the <a href="http://openguidedwaves.de/">Open Guided Waves</a> platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. </p> <p>A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.</p> <p><br> Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.</p> <p>The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.</p> <p>The detailed description of the dataset is published in Data in Brief Journal [3].</p>
Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets
<p>The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.</p>
A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files
<p><strong>Supplementary Fig. S1</strong>: θ<sub>evo</sub> calculated for each analyzed edge for specific OXPHOS complexes. Analyses were performed as in fig. 1F, except that SPCSs calculated from mtDNA-encoded protein positions in Complex I, Complex III, Complex IV, or Complex V were used to generate θevo values.</p> <p><strong>Supplementary Fig. S2</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 2A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p><strong>Supplementary Fig. S3</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median confidence intervals). Analysis was performed as in (<em>A</em>) fig. 2B or (<em>B</em>) fig. 2C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S4</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 3A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V subunits.</p> <p><strong>Supplementary Fig. S5</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by lower 90% median confidence limit). Analysis was performed as in fig. 3B, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S6</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by upper 90% median confidence limit). Analysis was performed as in fig. 3C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p>---</p> <p><strong>Supplementary File 1</strong>: All predicted protein substitutions along all edges at positions containing less than 2% gaps across input and ancestral sequences are listed, along with associated taxonomy information, TSS, and branch length. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 2</strong>: The TSS calculated for each mitochondrial protein alignment position. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 3</strong>: SPCS and θevo outputs are provided for analyses across all mitochondria-encoded positions, as well as for focused analyses of specific OXPHOS complexes and individual proteins.</p> <p><strong>Supplementary File 4</strong>: A GenBank flat file containing RefSeq entries for mammalian mtDNAs, as well as the entry for the reptile Anolis punctatus.</p> <p><strong>Supplementary File 5</strong>: A maximum likelihood inferred tree generated by a RAxML-NG analysis of concatenated and aligned protein coding sequences from mammalian and Anolis punctatusmtDNAs.</p> <p><strong>Supplementary File 6</strong>: Bootstrap replicates were generated from the alignment of concatenated protein coding sequences. Felsenstein’s Bootstrap Proportions (Felsenstein 1985) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 7</strong>: Bootstrap replicates were generated using concatenated mammalian mtDNA coding sequences. Transfer Bootstrap Expectations (Lemoine 2018) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 8</strong>: PAGAN tree output produced using aligned amino acid sequences and the rooted maximum likelihood inferred tree as input.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.