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High-Resolution Vegetation Distribution (HRVD) dataset
<p><strong>High-Resolution Vegetation Distribution (HRVD) dataset</strong> was developed by integrating multiple datasets, including the China multi-period land use/cover change remote sensing monitoring dataset (CNLUCC), MODIS MCD12Q1 land cover product, and Vegetation Atlas of China (1:1,000,000). The HRVD dataset provided vegetation distribution data in China in 2020 with a horizontal resolution of 1 km. <br><em>The HRVD dataset includes tree, shrub, herb, and crop, corresponding to the value of 1, 2, 3, and 4 in the attribute table, respectively.</em></p>
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
High-resolution global gridded population between 1870 and 2100
<p>history_pop.zip contains historical gridded population data from 1870 to 2010 at a 10-year interval</p> <p>SSP1.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP1</p> <p>SSP2.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP2</p> <p>SSP3.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP3</p> <p>SSP4.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP4</p> <p>SSP5.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP5</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
Simulation of Southern Ocean cloud processes using the high-resolution regional UK Met Office Unified Model with interactive aerosols
<p>Code and model data used to create the figures for Price et al., 2024, JGR (in prep.).</p>
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
<p>Codes and data made available as part of the data paper "HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)" publication. This work presents the HighResClimNevada database, a climatic database for Sierra Nevada (southern Spain) based on data modeled with the Weather Research and Forecasting model. The data used as a reference for the evaluation of HighResClimNevada are freely available online at the websites of the different institutions that develop it, so they are not available here.</p> <p>This research was financially supported by the project "Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)" funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan - NextGenerationEU and by the Regional Government of Andalucia, the project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE; and LifeWatch-2019-10-UGR-01 co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE) LifeWatch-ERIC action line; and P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades.</p>
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
<p>This dataset provides annual paddy rice maps of Northeast and Southeast Asia from 2017 to 2019.</p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: Northeast and Southeast Asia</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** CropType: Paddy rice.</p> <p>*** Year: Values from 2017 to 2019</p> <p>*** The file name consists of year (Y), latitude range (N/S), and longitude range (E). For example, the file name '2019Y_40_45N_140_145E' means the map of paddy rice in the range of 40-50 degrees North and 140-145 degrees East for 2019.</p>
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Figure 28 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 28 Comparison of the results of Snively et al. (2019) with the growth curve of Tyrannosaurus rex. A comparison of the agility values between T. rex and other tyrannosaurids. The values for T. bataar and D. torosus serve as predictive proxies for the corresponding values in subadult and young adult T. rex. The values for T. rex are calibrated to the growth series, but those of the other taxa are positioned relative to the values seen in T. rex. Inset of the data in table form shows the trends in the data; low values for Albertosaurus libratus are in boldface italics. Key to specimens numbered on the growth curve is in Fig. 12. Al, Albertosaurus libratus; Dt, Daspletosaurus torosus; Tb, Tyrannosaurus bataar; Tr, Tyrannosaurus rex. Full-size DOI: 10.7717/peerj.9192/fig-28
Figure 27 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 27 Comparison of the results of Henderson & Snively (2004) with the growth curve of Tyrannosaurus rex. The rotational inertia (RI) of smaller and progressively distant sister taxa of T. rex serve as predictive proxies for the RIs of young adult and juvenile T. rex. Given the larger size of T. rex in contrast to non-tyrannosaurine tyrannosaurids, the RI of young adult T. rex will almost certainly be more comparable to that of adult D. torosus than to adult A. libratus. The positions of the taxa, aside from T. rex (FMNH PR2081), are relative and are not intended to correspond to exact locations along the growth curve. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-27
Figure 24 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 24 Skull bending strength mapped onto the growth curve of Tyrannosaurus rex. The results of Snively, Henderson & Phillips (2006) showing that the subadult growth stage was an important functional transition point during ontogeny between the long and low skulls of adults and tall and sturdy skulls of more mature animals. Their results show a progression in strength of the skull frame and dentition throughout the adult categories. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-24
Figure 23 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 23 The results of Henderson (2002) mapped onto the growth curve of Tyrannosaurus rex. All measures of the correlates of orbital fenestra size and shape change from juvenile to adult categories. It is predicted here that this transition occurred early in ontogeny, at the subadult growth stage, given the presence of correlates of a tall skull in subadult specimens. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-23
Figure 21 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 21 Sex dimorphs and the taxon "Tyrannosaurus "x"" of Larson (2008) mapped onto the ontogram of Tyrannosaurus rex. A transitional pattern is not seen between gracile and robust morphs; if sexual dimorphism was present, then the "gracile" and "robust" morphs should group along separate branches, which is not seen. Also, specimens referred to the taxon "T. "x"" do not form a clade, indicating that it is not a valid taxon. The pattern seen here is what is expected for a species without sexual dimorphism. Specimens considered in Larson (2008) as gracile are in boldface italics with a boldface "G"; specimens considered in Larson (2008) as robust are in boldface with a boldface "R"; specimens considered in Larson (2008) as referable to "T. "x"" are in italics and marked with an "X". Full-size DOI: 10.7717/peerj.9192/fig-21
Figure 19 Bivariate scatterplot showing the relationship between geographic location with maturity among 28 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 19 Bivariate scatterplot showing the relationship between geographic location with maturity among 28 specimens Tyrannosaurus rex. Growth stages (x-axis) and geographic location (y-axis) have been converted to ranks. See Table 17 for the ranked data. "Montana North" refers to the region of Dawson, Garfield, and McCone counties, and "Montana South" refers to the region of Yellowstone and Carter counties. Maturity increases to the right along the x-axis; the y-axis follows the north-south axis of North America. Full-size DOI: 10.7717/peerj.9192/fig-19
Figure 15 Bivariate scatterplot showing the relationship between dentary tooth count with maturity among 16 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 15 Bivariate scatterplot showing the relationship between dentary tooth count with maturity among 16 specimens of Tyrannosaurus rex. Growth rank increases away from the origin (i.e., maturity increases to the right) and corresponds to growth stages for which dentary tooth count was available for a given specimen; that is, the rank does not correspond to growth stage. Dentary tooth rank corresponds to relative tooth count, where low ranks correspond to high tooth counts and low ranks correspond to high tooth counts. Full-size DOI: 10.7717/peerj.9192/fig-15
Figure 12 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 12 Tyrannosaurus rex ontogram, chronological age, and mass mapped onto the growth curve of Erickson et al. (2004). Ranges of growth categories are indicated across the top. Each circle represents an individual specimen; the vertical columns of circles indicate multiple specimens in a single growth stage; the horizontal position of the white circles does not imply a specific chronological age. For ease of interpretation, and to accommodate missing mass estimates, the position of the circles do not correspond with the scale of the y-axis. The gray circles indicate histologically aged specimens that are connected to the x-axis by dashed lines for ease of interpretation. The star represents the ~3,000 kg threshold that separates T. rex from its closest, but smaller, relatives. Alternating gray and white bars in the background block out the growth categories and their blurred edges reflect the imprecision of their boundaries. The height of each black vertical bar corresponds to the number of synontomorphies in each growth stage, which ranges from 1 to 90 characters. Asterisk indicates the type specimen of T. rex. Female symbols indicate BMRP 2006.6.4 and MOR 1125, the only unambiguous female specimens in the data set. Skull illustrations are to scale, with that of the adult set to 1.3 m. From left to right the skulls are: CMNH 7541, BMRP 2002.4.1, and AMNH FARB 5027. The large juvenile BMRP 2002.4.1 is in dorsal view to show the early occurrence of the narrow snout and wide temporal region that characterizes T. rex to the exclusion of other tyrannosaurids, which is ontogenetically incongruent with its late-occurring (i.e., autapomorphic) phylogenetic homolog; see text for discussion. Major life history events are indicated, including the onset of sexual maturity and somatic maturity, and the earliest occurrence of histologic adulthood. Suspensorium in CMNH 7541 is reconstructed after BMRP 2002.4.1. EFS, External Fundamental System. Specimens: 1, LACM 28471; 2, AMNH FARB 5050; 3, DDM 344.1; 4, CMNH 7541; 5, BMRP 2002.4.1; 6, RSM 2990.1; 7, BMRP 2006.4.4; 8, LACM 23845; 9, MOR 1125; 10, TMP 1981.006.0001; 11, LACM 150167; 12, AMNH FARB 5117; 13, UWBM 99000; 14, TMP 1981.012.0001; 15, RSM 2523.8; 16, SDSM 12047; 17, AMNH FARB 5027; 18, AMNH FARB 5029; 19, NHMUK R7994; 20, NMMNH P-3698; 21, MOR 1131; 22, MOR 980; 23, MOR 555; 24, LACM 23844; 25, CM 9380; 26, UCMP 118742; 27, MOR 008; 28, UMNH 11000; 29, MOR 2822; 30, UWGM 181; 31, FMNH PR2081. Full-size DOI: 10.7717/peerj.9192/fig-12
Figure 11 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 11 The frequency distribution of changes to the craniomandibular functional modules (sensu Werneburg et al., 2019) in the growth series of Tyrannosaurus rex. Growth stages are along the x-axis (corresponding to the numbered nodes of the ontogram in Fig. 2) and the y-axis corresponds to the number of synontomorphies. The onset of the changes to the skull roof, snout, mandibular ramus, and suspensorium modules occur early in growth, whereas the onset of changes to the parietal and braincase occur in adulthood. Changes continue throughout growth in all domains, aside from those to the parietal that cease at growth stage 14. Full-size DOI: 10.7717/peerj.9192/fig-11
Figure 31 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 31 Bivariate scatterplot showing the test of ontogenetic recapitulation of phylogenetic novelties in Tyrannosaurus rex. Growth stage rank (increases away from the origin) is along the x-axis; clade rank (increases away from the origin) is along the y-axis. If recapitulation is present, then the ranks will increase montonically from the origin. A recapitulatory pattern is not seen in T. rex; see text for details. Full-size DOI: 10.7717/peerj.9192/fig-31
Figure 22 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 22 Sex dimorphs of Larson (2008) mapped onto the growth curve of Tyrannosaurus rex. A transitional pattern is not seen between gracile and robust morphs; if sexual dimorphism was present, then the "gracile" and "robust" morphs should grade into each other, which is not seen. Likewise, an ontogenetic progression among the cranial and postcranial indices is not seen. See text for details. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-22
Figure 33 A in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 33 A simplified cladogram of living and extinct Archosauriformes showing 13 cranial and postcranial growth changes that are optimized as synapomorphies. Most of the growth changes are ancestral for Archosauriformes. The position of several characters at progressively exclusive clades is almost certainly an artifact of missing data (e.g., increase in mandible height, enlargement of muscle attachments, etc.) and they are predicted to be synapomorphic for Archosauriformes once the appropriate data are acquired. This comparison shows that highly derived species such as Tyrannosaurus rex do not deviate from the ancestral growth trends that first evolved in significantly smaller taxa. See text for sources; see Table 23 for the distribution of character states among the taxa. Full-size DOI: 10.7717/peerj.9192/fig-33
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.