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1,721 results for “network data”
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans
<p>Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).</p>
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments
<p>Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available.</p> <p>This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/).</p> <p>This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.</p>
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
Data from: Personality and social network structure influence cooperative dynamics across canid species
<p>In canids, cooperative behaviour occurs in many scenarios. However, most studies focus on single-species observations, not accounting for variation beyond the species-level. We modelled cooperative behaviour using Eigenvalue centrality as well as boldness combined with biological traits such as kinship, sex, age, mating system and foraging strategy in multiple canid species with Bayesian inference, Tukey HSD and distance correlation.</p>
OH-IIUNAM network precipitation data
<p>NetCDF files for all 51 working stations of the OH-IIUNAM (Observatorio Hidrológico del Instituto de Ingeniería de la Universidad Nacional Autónoma de México), a network of 39 laser disdrometers and 12 weighting rain gauges deployed across Mexico City.</p> <p>The OH-IIUNAM network relies on the OTT Parsivel<sup>2</sup> disdrometer and Pluvio<sup>2</sup> L weighing gauge<sup> </sup>instruments, which use a laser diode to produce a horizontal sheet of light to detect the number and size of hydrometeors and, a weight-based sensor to measure liquid and solid precipitation, respectively.</p>
Data from: Integrating pheromonal and spatial information in the amygdalo-hippocampal network. Villafranca-Faus et al. 2021
<p>The local field potential (LFP) of the dorsal hippocampus (CA1) and cortical amygdala (PMCo) of mice, under head-fix recording and inmersed on a virtual environtmernt.</p> <p><strong>Paper Abstract</strong>: <br> Vomeronasal information is critical in mice for territorial behavior. Consequently, learning the territorial spatial structure should incorporate the vomeronasal signals indicating individual identity into the hippocampal cognitive map. In this work we show in mice that navigating a virtual environment induces synchronic activity, with causality in both directionalities, between the vomeronasal amygdala and the dorsal CA1 of the hippocampus in the theta frequency range. The detection of urine stimuli induces synaptic plasticity in the vomeronasal pathway and the dorsal hippocampus, even in animals with experimentally induced anosmia. In the dorsal hippocampus, this plasticity is associated with the overexpression of pAKT and pGSK3β. An amygdalo-entorhino-hippocampal circuit likely underlies this effect of pheromonal information on hippocampal learning. This circuit likely constitutes the neural substrate of territorial behavior in mice, and it allows the integration of social and spatial information.</p>
Diversity in citations to a single study: Supplementary data set for citation context network analysis
<p><strong>Introduction</strong></p> <p>This document describes the data set used for all analyses in 'Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited' accepted for publication in <em>Quantitative Science Studies</em> [1].</p> <p><strong>Data Collection</strong></p> <p>The data collection procedure has been fully described [1]. Concisely, the data set contains bibliometric data collected from Web of Science Core Collection via the University of Edinburgh’s Library subscription concerning all papers that cited a cohort study, Paul <em>et al.</em> [2], in the period <1985. This includes a full list of citing papers, and the citations between these papers. Additionally, it includes textual passages (citation contexts) from 343 citing papers, which were manually recovered from the full-text documents accessible via the University of Edinburgh’s Library subscription. These data have been cleaned, converted into network readable datasets, and are coded into particular classifications reflecting content, which are described fully in the supplied code book and within the manuscript [1]. </p> <p><strong>Data description</strong></p> <p>All relevant data can be found in the attached file 'Supplementary_material_Leng_QSS_2021.xlsx', which contains the following five workbooks:</p> <ul> <li><strong>“Overview”</strong> includes a list of the content of the workbooks.</li> <li><strong>“Code Book”</strong> contains the coding rules and definitions used for the classification of findings and paper titles.</li> <li><strong>“Node attribute list”</strong> includes a workbook containing all node attributes for the citation network, which includes Paul et al. [2] and its citing papers as of 1984. Highlighted in yellow at the bottom of this workbook is two papers that were discarded due to duplication - remove these if analysing this dataset in a network analysis. The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Label</em>, the formal citation of the paper to which data within this row corresponds. Citation is in the following format: last name of first author, year of publication, journal of publication, volume number, start page, and DOI (if available). </li> <li><em>Title</em>, the paper title for the paper in question.</li> <li><em>Publication_year</em>, the year of publication.</li> <li><em>Document_type, </em>the document type (e.g. review, article)</li> <li><em>WoS_ID</em>, the paper’s unique Web of Science accession number.</li> <li><em>Citation_context</em>, a column specifying whether citation context data is available from that paper</li> <li><em>Explanans</em>, the title explanans terms for that paper;</li> <li><em>Explanandum</em>, the explanandum terms for that paper.</li> <li><em>Combined_Title_Classification</em>, the combined terms used for fig 2 of the published manuscript.</li> <li><em>Serum_cholesterol_(SC)</em>, a column identifying papers that cited the serum cholesterol findings.</li> <li><em>Blood_Pressure_(BP), </em>a column identifying papers that cited the blood pressure findings.</li> <li><em>Coffee_(C),</em> a column identifying papers that cited the coffee findings.</li> <li><em>Diet_(D), </em>a column identifying papers that cited the dietary findings.</li> <li><em>Smoking_(S), </em>a column identifying papers that cited the smoking findings.</li> <li><em>Alcohol_(A), </em>a column identifying papers that cited the alcohol findings.</li> <li><em>Physical_Activity_(PA),</em> a column identifying papers that cited the physical activity findings.</li> <li><em>Body_Fatness (BF), </em>a column identifying papers that cited the body fatness findings.</li> <li><em>Indegree,</em> the number of within network citations to that paper, calculated for the network shown in Fig 4 of the manuscript.</li> <li><em>Outdegree</em>, the number of within network references of that paper as calculated for the network in Fig 4.</li> <li><em>Main_component</em>, a column specifying whether a node is contained in the largest weakly connect component as shown in Fig 4 of the manuscript.</li> <li><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Fig 5).</li> </ol> <ul> <li><strong>“Edge list”</strong> includes a workbook including the edges for the network. The columns refer to:</li> </ul> <ol> <li><em>Source</em>, contains the node identifier of the citing paper.</li> <li><em>Target,</em> contains the node identifier of the cited paper.</li> </ol> <ul> <li><strong>“Citation context classification</strong>” includes a workbook containing the WoS accession number for the paper analysed, and any finding category discussed in that paper established via context analysis (see the code book for definitions). The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Finding_Class, </em>the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> <ul> <li><strong> “Citation context data”</strong> includes a workbook containing the WoS accession number for papers in which citation context data was available, the citation context passages, the reference number or format of Paul et al. within the citing paper, and the finding categories discussed in those contexts (see code book for definitions). The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Citation_context</em>, the passage copied from the full text of the citing paper containing discussion of the findings of Paul et al.</li> <li><em>Reference_in_citing_article</em>, the reference number or format of Paul et al. within the citing paper.</li> <li><em>Finding_class, </em>the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> <p><strong>Software recommended for analysis</strong></p> <p>For the analyses performed within the manuscript, Gephi version 0.9.2 was used [3], and both the edge and node lists are in a format that is easily read into this software. The Sci2 tool was used to parse data initially [4].</p> <p><strong>Notes</strong></p> <ol> <li>Leng, R. I. (Forthcoming). Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited. Quantitative Science Studies.</li> <li>Paul, O., Lepper, M. H., Phelan, W. H., Dupertuis, G. W., Macmillan, A., McKean, H., <em>et al.</em> (1963). A longitudinal study of coronary heart disease. <em>Circulation, </em><strong>28</strong>, 20-31. <a href="https://doi.org/10.1161/01.cir.28.1.20">https://doi.org/10.1161/01.cir.28.1.20</a>.</li> <li>Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.</li> <li>Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></li> </ol>
Data for: Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: an SEEG study
<p>Periventricular nodular heterotopias (PVNH) are areas of neurons abnormally located in the white matter that might be involved in physiological cortical functions. Autoscopic hallucinations are changes in self-consciousness determined by a mismatch in integration of multiple sensory inputs. Our goal is to highlight the brain network involved in generation of autoscopic hallucination elicited by electrical stimulation of a PVNH in a drug resistant epilepsy patient.</p> <p> Our patient was explored using stereo-electroencephalography with electrodes covering the right posterior temporal PVNH and the adjacent cortex. Direct electrical high frequency stimulation of the PVNH elicited autoscopic hallucinations mainly involving the face and upper trunk. We then used multiple modalities to determine brain connectivity: single pulse electrical stimulation of the PVNH and stimulation-evoked potentials were used to highlight resting state effective connectivity. High-frequency stimulation using alternating polarity pulses enabled us to identify the network involved, time-locked to the clinical effect and to map symptom-related effective connectivity. Functional connectivity using a non-linear regression method was used to determine dependencies between different cortical regions following the stimulation. Finally, structural connectivity was highlighted using deterministic fiber tracking.</p> <p>Multi-modal connectivity analysis identified a network involving the PVNH, occipital and temporal neocortex, fusiform gyrus and parietal cortex.</p>
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
DATA FOR STUDY OF PRIVACY ATTITUDE OF USERS OF SOCIAL NETWORKING SITES AND THEIR EXPECTATIONS FROM LAW IN INDIA
<p>In the present study, on Indian population a disproportionate, stratified, purposive, convenience mixed sampling technique has been adopted in order to ensure proper representation of all the stakeholders concerned with regard to the issue of data privacy in India among the population of the study. The stratified sampling technique is popularly used for a large size population and where it is desirable to purposively have an adequate representation of the all the sub-groups. It is estimated that there are about 0.2 million Law Enforcement Officers (Directors General of Police to Assistant Sub- Inspector), 2.2 million Legal Professionals, including 21,586 Judges, 1.5 million Academicians, about 5000 Information Assurance and Privacy Experts, and 450 million Internet Users in India, out of which 196 million use SNSs. It may be clarified here that all these stakeholders are not only the users of social networking sites but also Indian citizens who are stakeholders in enactment and implementation of the privacy law as and when it is enacted.</p> <p>Based on the size of the total population, a statistically adequate sample size of 385, having a 95 per cent Confidence Level, 5 per cent Margin of Error (Confidence Interval), 0.5 Standard Deviation, a 1.96 Z-score was calculated.</p> <p> </p> <p> </p> <p> </p>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
NGS competence network - pipeline benchmark data
<p>VCF files generated with the megSAP pipeline for a pipeline benchmark performed by the NGS competence network.</p>
Replication Data for: Geometry-Complete Perceptron Networks for 3D Molecular Graphs
<p>Included are preprocessed data files for the Newtonian many-body systems modeling task described in our accompanying manuscript.</p>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
<p>Files uploaded here are related to the paper titled "Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data". Here we investigate how generative adversarial networks can be used to match simulated- and experimental INS data.</p>
Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
Operator-Software Impact in Local Tie Networks: Case study at Geodetic Observatory Wettzell (Data set)
<p>The operator-software impact describes the differences between results introduced by different operators using identical software packages but applying different analysis strategies to the same data. This contribution studies the operator-software impact in the framework of local tie determination, and compares two different analysis approaches. Both approaches are used in present local tie determinations and mainly differ in the consideration of the vertical deflection within the network adjustment. However, no comparison study has yet been made so far. Selecting a suitable analysis approach is interpreted as a model selection problem, which is addressed by information criteria within this investigation. A suitable model is indicated by a sufficient goodness of fit and an adequate number of model parameters. Moreover, the stiffness of the networks is evaluated by means of principle component analysis. Based on the date of a measurement campaign performed at the Geodetic Observatory Wettzell in 2021, the impact of the analysis approach on local ties is investigated. For that purpose, an innovated procedure is introduced to obtain reference points of space geodetic techniques defining the local ties. Within the procedure, the reference points are defined independently of the used reference frame, and are based on geometrical conditions. Thus, the results depend only on the estimates of the performed network adjustment and, hence, the applied network analysis approach. The comparison of the horizontal coordinates of the determined reference points shows a high agreement. The differences are less than 0.2 mm. However, the vertical components differ by more than 1 mm, and exceed the coverage of the estimated standard deviations. The main reasons for these large discrepancies are a network tilting and a network bending, which is confirmed by a residual analysis.</p>
Socio-demographic portrait and demographic values of pro-natalists and anti-natalists in Russia: assessment based on data from the social network VKontakte
<p>The dataset contains data from personal profiles of social network users VKontakte - socio-demographic characteristics (gender, age, date of birth, marital status, country and city of residence) and indicators of demographic values (“the main thing in life”, attitude to alcohol, attitude to smoking, pronatalist or anti-natalist), as well as a number of other indicators - religious and political beliefs,<br> "the main thing in people." Initially, as a selection criterion, the authors used belonging user to the relevant communities (groups) of the VKontakte social network, sharing different family and reproductive values (to be a parent, to give birth child, start a family or profess the values of childfree philosophy). For example, persons who were members of groups devoted to "childfree" values were assigned the status "anti-natalist". And individuals in groups devoted to issues of childbirth and family values, parenthood, were assigned the status of "pronatalist". For example, groups with the names "GOOD PARENTS" (https://vk.com/club52388302) and "MOM: Development, Family, Children" (https://vk.com/club69716165) are pronatalist, and groups "The TRUTH about Childfree (Childfree)" (https://vk.com/club58565280) and "Overheard Childfree" (https://vk.com/club69265846) - to anti-natalist ones. The authors extracted comments of users of these two types of groups in the social network VKontakte, applying methods of extraction (parsing) of data from social networks using VK API. Organized open access to databases compiled by us from hundreds<br> thousand comments from users of dozens of groups of two types in more than one and a half decades [Kalabikhina &amp; Banin, 2020; Kalabikhina &amp; Banin, 2021]. Further, the authors linked the data of their personal data to users from these groups.<br> questionnaires. At the second stage, the resulting sample was additionally corrected - in it only those persons who filled in the field "The main thing in life" in the personal questionnaire on VKontakte to determine priority values for users with different types of reproductive behavior. Include in the analysis only those users who answered the question from personal questionnaire on VKontakte about what is most important for them in life, allows reduce the risk of "bots" getting into the sample. We believe that in the questionnaires of bots such fields as "The main thing in life", "The main thing in people" will not be filled in, the indication of this<br> information in the personal profile testifies in favor of the fact that the account is really belongs to a real person. At this stage, the sample included 754,315 users. Additionally, we limited the sample according to the age indicated in the questionnaire. We excluded from the sample users who indicated their age of 80 years and older. We we believe that this is an indirect sign by which it is possible to identify accounts with inaccurate information. The final sample after the introduced restrictions was 377,786 people, it includes users of the social network VKontakte, consisting of groups of anti-natalists and pro-natalists who filled in the information section about themselves item "The main thing in life" minus persons who indicated in the questionnaire that they are over 80 years old.</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.