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9,882 results for “scales”
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
Seasonal hindcast of temperature and precipitation at a local scale by using TeWA approach
<p><strong>Methodology</strong></p> <p>Data set of simulated time-series of temperature and precipitation for the 1982-2020 period. Our statistical seasonal prediction model have two main components: a) the ocean-atmosphere coupling represented by correlations between surface variables with delayed teleconnections and b) the self-predictability of the residual anomalies by trends or cycles (quasi-oscillations).</p> <p>The approach has three stages approach with two main predictor components, as mentioned above. The first two stages consist of separate predictions, one per each component, and the third stage is a combination of both predictions (Fig. 2): Teleconnection-based approach (Redolat et al. 2019, 2020) and a self-predictability by using Wavelet-ARIMA models (Conejo et al. 2005; Joo and Kim 2015). Therefore, the total method is a Teleconnection+Wavelet+ ARIMA (TeWA) approach.</p> <p><strong>References</strong></p> <p>Conejo, A.J., M.A. Plazas, R. Espinola, A.B. Molina, 2005: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst., 20, 1035-1042, https://doi.org/10.1109/TPWRS.2005.846054.</p> <p>Joo, T., S. Kim, 2015: Time series forecasting based on wavelet filtering. Expert Syst. Appl. 42, 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026</p> <p>Redolat, D., R. Monjo, C. Paradinas, J. Pórtoles, E. Gaitán, C. Prado-López, and J. Ribalaygua, 2020: Local decadal prediction according to statistical/dynamical approaches. Int. J. Climatol., 40: 5671–5687. https://doi.org/10.1002/joc.6543.</p> <p>Redolat, D.; R. Monjo, J.A. Lopez-Bustins, and J. Martin-Vide, 2019: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theor. Appl. Climatol., 135: 1059–1077. https://doi.org/10.1007/s00704-018-2424-6.</p>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Dataset of The latent factor structure and assessment of childbirth-related PTSD in fathers and co-parents: psychometric characteristics of the City Birth Trauma Scale – French version (partner version)
<p>Little is known about the latent factor structure of CB-PTSD symptoms in co-parents (i.e., (a non-expecting mother or father). The City Birth Trauma Scale (City BiTS) was developed to assess childbirth-related posttraumatic stress disorder following childbirth (CB-PTSD), based on the PTSD criteria of the DSM-5. Still, no validated French questionnaire exists to assess CB-PTSD symptoms in co-parents. This study aimed (1) to establish the latent factor structure of CB-PTSD, and (2) to validate the French version of the City BiTS (partner version). </p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 282 co-parents who had an infant within the last 12 months. Sociodemographic data such as age, marital status, educational level, weeks of gestation, type of delivery, history of traumatic childbirth, or history of a traumatic event is available. </p>
Dia-Pol: A large scale BlackLivesMatter and MeToo Twitter dataset
<p>This dataset (tweets_id_list.json) contains 258609 number of tweets sent in English extracted from Twitter API using the query word “#blacklivesmatter.” The dataset spans the period from 2020-01-01 to 2021-12-31 and was retrieved on 2022-06-10.</p>
Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations
<p>These are the raw or processed data used for a paper published in Chemical Geology by Debrie et al. (2022), entitled "Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations", <a href="https://doi.org/10.1016/j.chemgeo.2022.121059">https://doi.org/10.1016/j.chemgeo.2022.121059</a></p> <p>The data content is summarized in the List_description_of_data.xlsx file</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
The soil province geodatabase of Italy, storing information of soil typological units and broad soil regions at the 1:1,000,000 and 1:10,000,000 scales
<p>The Soil Map of Italy at 1:1,000,000 scale, was the result of the work of Edoardo AC Costantini, Giovanni L'Abate, Roberto Barbetti, Maria Fantappié, Romina Lorenzetti, and Simona Magini affiliated to Research Centre for agrobiology and soil science (CREA-ABP), in collaboration with several regional institutions, universities and other research centers of the CREA - Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria. The map, was printed by S.EL.CA. of Florence. The map is an informative and educational work of general scientific interest, which updates the previous one edited by prof. Fiorenzo Mancini and collaborators in 1966 both in terms of knowledge and of the adopted methods. It was produced processing of all data within a geographical and soil geodatabase, collected by the CREA-ABP and other institutions collaborating in over ten years of work and using the latest international methods. The soil map shows the distribution of major soils in the country and constitutes a milestone in the process launched in 1999 as part of the project the Soil Map of Italy at a scale of 1: 250,000, funded by MIPAAF and implemented in collaboration with the regional institutions. Both broad soil regions and soil provinces (reference scale 1:10,000,000 and 1:1,000,000) are reported.</p> <p>Most small-scale soil maps report dominant typological units and allow only a partial appraisal of pedodiversity since territories with similar dominant soils can actually possess different pedodiversity. This is particularly true at the national scale, where a great wealth of soil information collected at more detailed scales is generalized.</p> <p>A methodology was set up, which aimed at preserving pedodiversity in upscaling soil maps by using geomatic techniques and the World Reference Base for soil resources (WRB). The main source of information was the soil system geodatabase of Italy, storing information of soil typological units and soilscapes at the 1:500,000 reference scale. Qualitative aggregation of soil taxa followed upscaling rules aimed at (i) maintaining the information about pedogenetic processes and (ii) grouping soilscapes showing recurrent patterns of soil forming processes. The upscaling methodology can be summarized in seven steps as follows: (1) soil forming processes selection, retrieved from soil typological units stored in the national database; (2) upscaling soil systems and creation of broad soil regions at 1:10,000,000 reference scale; (3) semantic upscaling of typological units to form taxa showing different soil forming processes; (4) ranking and associating soil forming processes; (5) geography upscaling of soil systems geometry to form polygons at 1:1,000,000 reference scale, called subregions; (6) ranking subregions according to their extension; (7) naming subregions by ranking the taxa according to the number of soil typological units.</p> <p>The soil subregion map reported 47 map unit and 148 taxa, belonging to 22 reference soil group of WRB and showing from one to four qualifiers. Each map unit had from 2 to 18 taxa, for a total of 317 occurrences. Thirty taxa had 3 or more occurrences, while the remaining took place in one or two subregions only.</p>
Small-scale fisheries adaptations understudied in climate change hotspots - database
<p>Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.</p>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
Data and script for "On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"
<p>Data and R script necessary to replicate the results of Riva et al. 2024 ("On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"; minor revisions, Biological Conservation).</p>
Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup
<p><strong>Abstract </strong></p> <p>We present a revised global plate motion model with continuously closing plate boundaries ranging from the Triassic at 230 Ma to the present day, assess differences between alternative absolute plate motion models, and review global tectonic events. Relatively high mean absolute plate motion rates around 9–10 cm yr-1 between 140 and 120 Ma may be related to transient plate motion accelerations driven by the successive emplacement of a sequence of large igneous provinces during that time. A ~100 Ma event is most clearly expressed in the Indian Ocean and may reflect the initiation of Andean-style subduction along southern continental Eurasia, while an ~80 Ma acceleration of mean rates from 6 to 8 cm yr-1 reflects the initial northward acceleration of India and simultaneous speedups of plates in the Pacific. An event at ~50 Ma expressed in relative, and some absolute plate motion changes around the globe and in a reduction of global mean velocities from about 6 to 4–5 cm yr-1, indicates that an increase in collisional forces (such as the India-Eurasia collision) and ridge subduction events in the Pacific (such as the Izanagi-Pacific Ridge) play a significant role in modulating plate velocities.</p> <p><strong>Muller et al. (2016) AREPS model file versions</strong></p> <p>This model has been maintained for some time after initial publication. There are six versions of the model that we provide, including:</p> <ul> <li>v1.10 – Some minor fixes were made to plate topologies, and so conforms to the originally-published model.</li> <li>v1.11 – A back-arc basin north of Arabia was introduced in the Cretaceous (see note below), and hence slightly diverges from the original model in plate topologies, velocities, and seafloor age-grids for this region.</li> <li>v1.14 – The latest version of the model that has duplicated topology segments cleaned from the evolving polygons, which helps with quantifying plate boundary lengths in the resolved topology output.</li> <li>v1.15 – The correction to the pre-83 Ma Pacific rotations according to Torsvik et al. (2019) has been applied.</li> <li>v1.16 – Some fixes to topologies</li> <li>v1.17 – Major update to the seafloor age-grids and topologies. Age-grids are consistent with v1.15 and 1.16 as well. We strongly recommend you use this version of the model.</li> </ul> <p>Note about the evolution of the western Tethys in this model: The Western Tethys, north of Arabia, is punctuated by ophiolite formation and obduction in Cretaceous times. The first end-member involves applying the central and eastern Tethys analogues of back-arc opening and closure following ophiolite obduction, much like is usually implied in the Kohistan-Ladakh and Greater India collision zone. This scenario makes the Western Tethys north of Arabia consistent with the model of the eastern Tethys. However, a second end-member interpretation for the formation of many of the ophiolites in the region is that they develop when a mid-oceanic ridge inverts to become a subduction zone. Both options are plausible, but we implemented a change in this plate model after it was published to reflect the first end-member scenario in order to link the region to the eastern Tethys in a plausible way. This scenario is based on back-arc opening from ~125 Ma (Jolivet et al., 2016), with subduction of back-arc initiating in Albian times from ~110 Ma (Ghazi et at., 2003; Aygul et al., 2015). Obduction and Arabia collision with an arc occurs at 85 Ma (Jolivet et al., 2016; Jagoutz et al., 2016). The scenario is also consistent with the recent work of Morris et al. (2016) on the Oman Ophiolite.</p> <p> </p> <p>The agegrids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/</a></p>
Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale
<p>Continental Europe surface lithology based on <strong><a href="http://www.europe-geology.eu/onshore-geology/geological-map/onegeologyeurope/">EGDI / OneGeology map</a></strong> at 1:1M scale produced by <a href="https://egdi.geology.cz/record/basic/5729ffdf-2558-48fc-a5d2-645a0a010855">GEOZS, Slovenia</a>. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and <strong><a href="https://inspire.ec.europa.eu/codelist/LithologyValue/">INSPIRE Lithology</a></strong> and Geochronologic Era URI codelists. Layers include:</p> <ul> <li>EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;</li> <li>dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;</li> </ul> <p>Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.</p> <p>Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.</p>
Parker Solar Probe Filtered Ion Scale Wave Activity for Encounters 8 to 16
<p>The following datasets are the result of filtering algorithm applied to a wave analysis of Parker Solar Probe data from Encounters 8 to 16. The wave analysis was conducted by Kristoff Paulson using a Short-Time Fourier Transform (STFT) approach based on polarization techniques derived by Means, 1972 (DOI: <a href="http://doi.org/10.1029/JA077i028p05551">10.1029/JA077i028p055511135</a>). Included is a jupyter notebook containing the filtering algorithm, the results of the filtering, and a demonstration of how to best open the files. The dataset for each encounter contains 9 columns that correspond with:</p> <ol> <li>Date in CDF epoch</li> <li>Left-handed (LH) Integrated Wave Power (nT^2) where integration is over frequency space (0-32 Hz) of filtered activity</li> <li>Right-handed (RH) Integrated Wave Power (nT^2)</li> <li>LH median ellipticity where median is over frequency space</li> <li>RH median ellipticity</li> <li>LH median coherency</li> <li>RH median coherency</li> <li>LH median wave normal angle (deg)</li> <li>RH median wave normal angle (deg)</li> </ol> <p>In all cases, ellipticity is measured in the Parker Solar Probe spacecraft frame. Ellipticity measures the ellipticity of the polarization ellipse and takes on values between -1 and 1. Values of 1 correspond with RH circular polarization and -1 with LH circular polarization. Coherency takes on values between 0 and 1. It measures how interrelated fluctuations are where 0 represents noise and 1 represents coherent fluctuations. The wave normal angle is the angle between the wave vector, k, and the local mean magnetic field, B. Since there are inherent ambiguities in the direction of the wave vector for single spacecraft measurements, the wave normal angle is calculated such that it takes on angles from 0 to 90 degrees. The filtering algorithm selects activity in which coherency is above 0.8, absolute value of ellipticity is above 0.5, and wave normal angle is below 45 degrees such that coherent, circularly polarized, near parallel propagating wave activity on ion scales is selected. <strong>If wave power for a given time has value of 0.0, then no fluctuations in the magnetic field data passed the required filters at that time.</strong></p> <p>:</p>
Supplementary table 1 for 'Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands' (2015)
<p>This supplementary table to Visser(2015) was not openly available. This dataset provides the supplementary table in the open ODS-format and also as XLS and CSV.</p> <div> <div>Publication: Visser, RM. 2015 Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands. <em>Journal of Archaeological Science</em> 53: 243–254. DOI: <a href="https://doi.org/10.1016/j.jas.2014.10.017">https://doi.org/10.1016/j.jas.2014.10.017</a>.</div> </div>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics
<p>ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics</p> <p>Supplementary file (full dataset download)</p> <p>- v2 with SMILES errors fixed (19.01.2019)</p>
Dataset _ Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Dataset _ Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</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.