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Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p><p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.</strong></p><p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p><p><strong>What is in the files?</strong></p><p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p><p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p><p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p><p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p><p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Additive interfacial chiral interaction in multilayers for stabilization of small individual skyrmion at room temperature
<p>International audience Facing the ever-growing demand for data storage will most probably require a new paradigm. Nanoscale magnetic skyrmions are anticipated to solve this issue as they are arguably the smallest spin textures in magnetic thin films in nature. We designed cobalt-based multilayered thin films where the cobalt layer is sandwiched between two heavy metals providing additive interfacial Dzyaloshinskii-Moriya interactions, which reach a value close to 2 mJ m-2 in the case of the Ir|Co|Pt asymmetric multilayers. Using a magnetization-sensitive scanning x-ray transmission microscopy technique, we imaged small magnetic domains at very low field in these multilayers. The study of their behavior in perpendicular magnetic field allows us to conclude that they are actually magnetic skyrmions stabilized by the large Dzyaloshinskii-Moriya interaction. This discovery of stable sub-100 nm individual skyrmions at room temperature in a technologically relevant material opens the way for device applications in a near future. on</p>
Pinning and movement of individual nanoscale magnetic skyrmions via defects
<p>An understanding of the pinning of magnetic skyrmions to defects is crucial for the development of<br> future spintronic applications. While pinning is desirable for a precise positioning of magnetic<br> skyrmions it is detrimental when they are to be moved through a material.Weuse scanning tunneling<br> microscopy (STM) to study the interaction between atomic scale defects and magnetic skyrmions that<br> are only a few nanometers in diameter. The studied pinning centers range from single atom inlayer<br> defects and adatoms to clusters adsorbed on the surface of our model system.Wefind very different<br> pinning strengths and identify preferred positions of the skyrmion. The interaction between a cluster<br> and a skyrmion can be sufficiently strong for the skyrmion to follow when the cluster is moved across<br> the surface by lateral manipulation with the STMtip.</p>
Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms
<h2>Contents</h2> <ul> <li><strong>codes.zip</strong> <ul> <li>For ECG feature extraction (this will need original ECG signal data) <ul> <li>ecg_feature_extraction.sh</li> <li>ecg_feature_extraction.py</li> <li>feature_extractor.py</li> </ul> </li> <li>For training with hyperparameter optimization <ul> <li>train.sh</li> <li>train.py</li> </ul> </li> <li>For prediction of prediabetes/diabetes from ECG feature <ul> <li>test.sh</li> <li>test.py</li> </ul> </li> </ul> </li> <li><strong>raw_ecg_data.zip</strong>: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 4.88 µV.)</li> <li><strong>external_ecg_data.zip</strong>: 2,456 ECG records used in our external validation. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 1 µV.)</li> <li><strong>participant_characteristics.csv</strong>: Health check records of 16,766 participants where the information below are stored. <ul> <li>participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.</li> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>age: The age of each participant at the time of the health checkup</li> <li>male_sex: If the participant is male, "True" is recorded</li> <li>smoking: if the participant smokes, "True" is recorded</li> <li>drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking</li> <li>height: participant's height in centimeters (cm)</li> <li>weight: participant's body weight in kilograms (kg)</li> <li>BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2</li> <li>pulse_rate: pulse rate in pulse per minute (/min) </li> <li>sBP: systolic blood pressure in mmHg</li> <li>dBP: diastolic blood pressure in mmHg</li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>development_data: "True" in records used as development data in our study</li> </ul> </li> <li><strong>external_cohort_characteristics.csv</strong>: Health check records of 2,456 participants where the information below are stored. <ul> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> </ul> </li> <li><strong>ecg_feature_data.zip</strong>: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG <ul> <li>ecg_features_1-lead.csv [Single-lead (lead I) ECG]</li> <li>ecg_features_12-lead.csv [12-lead ECG]</li> <li>ecg_features_12-leads_external_cohort.csv [12-lead ECG of external cohort]</li> </ul> </li> <li><strong>feature_list.zip</strong>: List of ECG features used (to be used for ECG extraction for original data) <ul> <li>feature_list_269_12-lead.csv [269 features for 12-lead ECG analysis]</li> <li>feature_list_28_1-lead.csv   [28 features for single-lead (lead I) analysis]</li> </ul> </li> <li><strong>model_12-lead.zip, model_1-lead.zip</strong>: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test<br> <ul> <li>model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation</li> <li>average_threshold.pkl : classification threshold, which is the average of 10-fold</li> </ul> </li> </ul> <p>Codes and data for demo are also available in (https://github.com/dkoga4116/diabetes_detector)</p>
Individual Table for Tel Aviv 2040 in SimMobility MIT Preday
<p>This table offers insights into the synthetic population of the Tel Aviv Metropolis projected for 2040. It serves as a forecast derived from various predictions by Israeli government officials. Primarily designed for research purposes, it is especially intended to be utilized as input for the SimMobility MIT demand simulator.</p>
Full Body Motion Capture of Single Individuals Following External Perturbations from Different Directions
<p>This dataset is composed of C3D files corresponding to full body motion of participants undergoing external perturbation at shoulder height with different sensory conditions. The temporal force profiles of the perturbations are also available.</p> <p>The following experiment received ethical approval from an ethics committee and all participants signed an informed consent form relative to the processing of their data. <br>The experiments were carried on 21 healthy young adults (10 females, 11 males). All were between 20 and 38 yo with a mean age of 27.2 (std: 4.2). Mean mass was 70.2 (std: 12.1) kg and height was 1.74 (std: 0.08) m. </p> <p>Participants motion was recorded using 45 reflective markers and a 23 Qualisys camera system (200Hz). <br>The markers were placed on participants following standardised anatomical landmarks. <br>The output signal of the force sensor was processed using a Butterworth low pass filter with a 5Hz cutoff frequency without phase shift. <br>The force sensor was synchronised with the motion capture software.<br>Tree reflective markers were also placed along the pole in order to retrieve the exact direction of the perturbations. </p>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Audio recordings of COVID-19 positive individuals from the prospective Predi-COVID cohort study with their ageusia and anosmia status
<p>We uploaded 1636 audio recordings originating from 259 distinct participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. The audios have been converted from their original format into WAV files. The audio name structure integrates the participant ID, the recording date and time of the audio recording, the type of audio (Type 1: reading of a text, Type2: hold the [a] vowel), the original audio format, and the symptomatic status for ageusia and anosmia (1: symptomatic, 0: asymptomatic) as such:</p> <p>predi-covid_{participant}{recording date and time}{type of audio}{original format}{sympyomatic status}.wav</p>
Most Serious Global Problem: Climate Change (Percentage of European Individuals)
<p>Most serious global problem: Climate Change<br> Percentage of individuals choosing it in European countries. Calculated from the Eurobarometer survey.</p> <p> </p>
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
RookID: an annotated dataset of vocalisations produced by individually-identified rooks housed together in an outdoors aviary in France
<p>A dataset of annotated recordings of a captive colony of rooks, recorded in Strasbourg, France in 2020 and 2021. Each rook was individually identifiable with leg rings. All recordings were taken in the morning a few hours after sunrise, when the birds were most vocally active. The colony was housed outdoors, so other noises are present, including both biotic (most notably various birds, human voices, and other animals) and abiotic (mostly car and train noises).</p> <p>Audio files (.wav): recorded at 48 kHz, 16-bit using 1 to 3 Song Meter 4 recorders (Wildlife Acoustics). Each recorder had two microphone with different gains to maximise dynamic range. The files were then manually synchronised and merged into multichannel (2 to 6) files.</p> <p>Label files (.tsv): Labels corresponding to each recording (each pair has the same name), noting the time stamps and individual emitter for each vocalisation. A single observer annotated all the recordings. Only rook vocalisations from the captive colony were annotated, not other bird vocalisations or the various noises in the data. The annotations consist of tables with 5 columns: </p> <ul> <li>Source: the individual producing the vocalisation. Note that only the bird's name is indicated. "Inc" and "Pls" are special cases: the first was for when identity could not be determined, the second when multiple individuals vocalised at once in such a manner that individuals could not be separated</li> <li>Start: starting time point for the vocalisation, in seconds (determined as the earliest point when the vocalisation was heard on any channel)</li> <li>End: ending time point for the vocalisation, in seconds (determined as the last point when the vocalisation was head on any channel)</li> <li>Event: gives information for the bird's activity at the time of the vocalisation, but largely in abbreviated form. One particular case is "sing", which correspond to vocalisations part of a song bout (which are defined as sequences of different vocalisations separated by less than approximately 10 seconds).</li> <li>Comment: other observations regarding the vocalisation. These are usually not standardised compared to the Event column. One special case is for "Pls": the Comment column then bears information regarding the identity of the individuals involved.</li> </ul> <p> </p> <p>This dataset was used in our article "Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks", to train neural networks to identify individual rooks. The dataset was therefore randomly split into train-validation-test datasets. For reproducibility, we provide the "splitting.csv" which contains the information pertaining to which files go in each dataset, and two scripts to do the split automatically.</p> <p>To do so: download and unpack the RookID folder somewhere on your computer, then download splitting.csv and either of the scripts to the same location. Both scripts will MOVE, not copy, the files to new folders corresponding to each dataset.</p> <ul> <li>with split_data.R: open the scrip in an RStudio environment, edit the out_path variable to the desired location, and run the script</li> <li>with split_data.py: run the following command line: python /path/to/split_data.py --out_path path/to/desired/location (note that the script will automatically create the necessary tree structure)</li> <li>Both scripts can be run without editing the out_path variables, in which case the new folders will be created at the same location</li> </ul> <p> </p> <p>For further information, see our code at <a href="https://gitlab.com/kimartin/rook-vocalisation-detection">https://gitlab.com/kimartin/rook-vocalisation-detection</a></p> <p>For any inquiries, please contact Killian Martin (<a href="mailto:killian.martin@ens-lyon.fr?subject=Inquiry%20about%20the%20RookID%20dataset">killian.martin@ens-lyon.fr</a>)</p>
Individual-donor scRNA-Seq datasets, as Seurat 4.0.5 objects
<p>The provided datasets correspond to the analyses of individual donor single-cell RNA Sequencing (scRNA-Seq) datasets, before their integration. The datasets have been saved as Seurat v4.0.5 objects. For clustering, we used default settings in Seurat 4.0.5 (resolution 0.8) and increased resolution, if necessary, to separate epithelium in proximal and distal. </p> <p>The *_clusters.pdf files show the suggested clusters in the individual datasets and the *_indiv_anno1.pdf files show the cell annotations according to the 84 cell states, described in the study with title "Developmental origins of cell heterogeneity in the human lung" (1st preprint version doi: https://doi.org/10.1101/2022.01.11.475631).</p> <p>The "*_cluster_annotations.csv" files provide information about the suggested annotations of the clusters.</p> <p>The "*_object_raw_and_log_counts.RData" objects contain the metadata and the UMI-counts [raw and log2(counts+1)] for each donor scRNA-Seq dataset.</p> <p> </p>
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
Individual tree data of the temporary test plot Neusorgefeld 5138 - VERMOS project
<p>This dataset is an artificial dataset of the dataset of the temporary trial plot in Neusorgefeld 5138 from the VERMOS project. The original dataset is significantly larger, the adaptation was made for a planned publication by Chris Wudel and was also carried out by him.</p>
Dataset: Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs
<p>Dataset used in the statistical analysis of the publication "Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs (<em>Microcebus murinus</em>)"</p> <p><strong>Abstract</strong></p> <p>Vocali<span>z</span>ations coordinate social interactions between conspecifics by conveying information concerning the individual or group identity of the sender. Social accommodation is a form of vocal learning where social affinity is signalled by converging or diverging vocali<span>z</span>ations to those of conspecifics. To investigate whether social accommodation is linked to the social lifestyle of the sender, we investigated sex-specific differences in social accommodation in a dispersed living primate, the grey mouse lemur, where females form stable sleeping groups whereas males live solitarily. We used 482 trill calls of 36 individuals from our captive breeding colony to compare acoustic dissimilarity between individuals with genetic relatedness, social contact time and body weight. Our results showed that female trills become more similar the more time females spen<span>d</span> with each other independent of genetic relationship, suggesting vocal convergence. In contrast, male trills were affected more by genetic than social factors. However, focus<span>s</span>ing only on sociali<span>z</span>ed males, male trills diverged from each other the more time males were cage partners. Thus, grey mouse lemurs show the capacity for social accommodation, with females converging their trills to signal social closeness to sleeping group partners, whereas males do not adapt or diverge their trills to signal individual distinctiveness. </p> <p> </p> <p>For details concerning the recording of the trills confer to the publication at doi: 10.1098/rstb.2023.0193</p>
Individual-based body sizes of wild bees along elevational gradients on Mt. Kilimanjaro
<p><span>This dataset contains body size measurements of wild bees that were captured along elevational gradients on the southern slopes of Mt. Kilimanjaro (Tanzania) using standardized sampling methods (pan traps, transect walks). The dataset includes bee species identified at the species level as well as morphospecies. The bees were measured individually, meaning that intraspecific differences in body size are also represented. The intertegular distance (ITD) in millimeters was measured as a surrogate for body size.</span></p> <p><span>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</span></p>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</p>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</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.