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22,922 results for “collections as data”
Fig. 1. Camera trap data was collected from 14 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 1. Camera trap data was collected from 14 protected areas within Thailand. NP = national park; WS = wildlife sanctuary; NH = non-hunting area.
Figs 13-18 in New data on the Afrotropical Xantholinini. 1. New species from South Africa in the Janak collection (Coleoptera, Staphylinidae) 285° contribution to the knowledge of the Staphylinidae
Figs 13-18: Tergite and sternite of the male genital segment, aedeagus of Notolinopsis khoi nov.sp. (13-15) and Notolinopsis lemniscatus nov.sp. (16-18) (scale bar: 0.1 mm).
Figs 7-12 in New data on the Afrotropical Xantholinini. 1. New species from South Africa in the Janak collection (Coleoptera, Staphylinidae) 285° contribution to the knowledge of the Staphylinidae
Figs 7-12: Tergite and sternite of the male genital segment, aedeagus of Notolinopsis mbotyianus nov.sp. (7-9) and Notolinopsis mirabilis nov.sp. (10-12) (scale bar: 0.1 mm).
Figs 1-6 in New data on the Afrotropical Xantholinini. 1. New species from South Africa in the Janak collection (Coleoptera, Staphylinidae) 285° contribution to the knowledge of the Staphylinidae
Figs 1-6: Tergite and sternite of the male genital segment, aedeagus of Xanthophius janaki nov.sp. (1-3) and Notolinopsis janaki nov.sp. (4-6) (scale bar: 0.1 mm).
Footactile rhythmics: protocols and data collection.
<p>The data shared refer to research investigating the relationship between human behaviour and space with the technological mediation of <em>footactile rhythms</em>.</p> <p>The documents describe the nine protocols devised and show the collection of qualitative (questionnaires) and quantitative (movement analysis, skin conductance) data.</p>
Data Collection - Overturning in the Subpolar North Atlantic Program (OSNAP)
<p>The data set is a collection of observations carried out under the framework of the Overturning in the Subpolar North Atlantic Program (OSNAP) international program, which is designed to provide continuous records of water hydrographic properties and currents in the subpolar North Atlantic. The data are logged by moored instruments and floats. The array of moored instruments is aligned along two transects / legs. The first one extends from the Labrador coast to the southwestern tip of Greenland across the mouth of the Labrador Sea (OSNAP West). The second transect is orientated from the southeastern tip of Greenland to Scotland (OSNAP East).</p> <p>Archived in this repository is the collection of moored data from the OSNAP West, which covers the periods 2014-2016, 2016-2018, 2018-2020. Five types of instruments were recovered from nine moorings: Microcats, Onsets, ADCPs, RCM-8 and RCM-11. The data have been quality-controlled and are stored in NetCDF data format. The nine moorings were deployed in different years at the four OSNAP sites on the Labrador shelf: K7, C1, C2 and C3. The collected data span the vertical range from 40 to 800 m. The coordinates, times and other essential metadata are stored in the attributes of the data files and the data inventory (file: OSNAP_Running_Log.xlsx).</p> <p>This repository includes static named branch releases which can be cited and pointed to by DOI along with the master branch that includes the latest updated content past the most recent named branch release.</p>
Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru
<p>Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru</p>
FIG. 1 in New data on the distribution of the two mole species Talpa aquitania Nicolas, Matinez-Vargas & Hugot, 2017 and T. europaea Linnaeus, 1758 in France based on museum and newly collected specimens
FIG. 1. — Map of the distribution of Talpa aquitania Nicolas, Martínez-Vargas & Hugot, 2017 (◼) and T. europaea Linnaeus, 1758 (▲) in France based on 1058 verified species records. The Loire River is indicated in blue, and department boundaries are in black.
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data
<p>The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021.</p> <p>The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf.</p> <p>Questionnaire Part 1, data file: all_usecases_wide.csv</p> <p>The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 2, data file: all_real_projects_scores.csv</p> <p>The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 3, data file: all_extrem_values_wide.csv</p> <p>The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br> </p>
Data for: Intergenerational genotypic interactions drive collective behavioural cycles in a social insect
<p>Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological, and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely but more surprisingly, indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).</p>
Data for: From individual behaviors to collective outcomes: fruiting body formation in Dictyostelium as a group-level phenotype
<p>Collective phenotypes, which arise from the interactions among individuals, can be important for the evolution of higher levels of biological organization. However, how a group's composition determines its collective phenotype remains poorly understood. When starved, cells of the social amoeba <em>Dictyostelium discoideum</em> cooperate to build a multicellular fruiting body, and the morphology of the fruiting body is likely advantageous to the surviving spores. We assessed how the number of strains, as well as their genetic and geographic relationships to one another, impact the group's morphology and productivity. We find that some strains consistently enhance or detract from the productivity of their groups, regardless of the identity of the other group members. We also detect extensive pairwise and higher-order genotype interactions, which collectively have a large influence on the group phenotype. Whereas previous work in <em>Dictyostelium</em> has focused almost exclusively on whether spore production is equitable when strains cooperate to form multicellular fruiting bodies, our results suggest a previously unrecognized impact of chimeric co-development on the group phenotype. Our results demonstrate how interactions among members of a group influence collective phenotypes and how group phenotypes might in turn impact selection on the individual.</p>
Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis
<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>
Solanum pimpinellifolium input data collected from the TPA - used for GWAS
<p>The GWAS input data used for mapping the candidate genes in S. pimpinellifolium collection, exposed to salt stress in The Plant Accelerator experiment (TPA). The phenotypic data was collected in an experiment was performed by Mitchell Morton while being a PhD student in the group of Prof. Mark Tester at KAUST. The genotypic data was collected by Magdalena Julkowska, and used for sequencing. The SNPs were called by Elodie Ray, and subsequently curated by Magdalena Julkowska for GWAS analysis. The GWAS was conducted by Magdalena Julkowska. </p>
Data for: Coordination of care is facilitated by delayed feeding and collective arrivals in the long-tailed tit
<p>When multiple carers invest in a shared brood, there is likely to be conflict among individuals over how much each carer invests. This conflict results in suboptimal investment to the detriment of all carers. It has been proposed that conditional cooperation, i.e. 'turn-taking' or 'alternation', may resolve this conflict by preventing exploitation. This contentious idea has received some empirical support, but distinguishing active alternation from that expected via passive processes has proved challenging. The aim of this study was to use detailed observations of provisioning to examine whether carers at biparental (parents only) and cooperative (parents and helpers) nests of the long-tailed tit <em>Aegithalos</em> <em>caudatus</em> behave in a context-dependent manner that enhances the level of alternation. First, we show that carers who had been the last to feed waited near the nest (loitering) for longer before feeding when they next arrived at the nest and allowed others to feed first, thus facilitating alternation. Secondly, we found that the arrival of carers near the nest and their subsequent feeds were tightly synchronised, with overlapping loitering periods, allowing them to monitor the effort of other carers. Finally, we show that measures of coordination were influenced by carers arriving in a status-dependent order, with breeding females consistently arriving first and helpers last. Together, these results show how patterns of alternation and synchrony arise in long-tailed tits and reveal the behavioural mechanisms underpinning coordination of care.</p>
Data collected from Vietnam
<p>This dataset contains qualitative and quantitative data that has been collected in Vietnam, the first page is the summary of all the information.</p>
ADCP data collected in the Southern California Bight by SWIFT drifters as part of the ONR "Langmuir Circulation Departmental Research Initiative (LC-DRI)"
<p>This is the public archive for ADCP data collected with SWIFT drifters during the 'Langmuir Circulation' Office of Naval Research Departmental Research Initiative (LC-DRI) field experiment, conducted between March 19th and April 6th, 2017 in the Southern California Bight 40 km west of Catalina Island. SWIFTs were deployed and recovered from the R/V R.G. Sproul during cruise SP1709 (Cruise DOI: 10.7284/907464). SWIFTs were deployed during storms with wind speeds up to 20 m/s and sampled strong diurnal warm layers during weaker wind periods.</p>
CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)
<p>The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states.</p> <p>This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively.</p> <p>Included features are:</p> <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>
Data Management Training Clearinghouse Metadata and Collection Statistics Report
<p>This collection contains a snapshot of the learning resource metadata from ESIP's <a href="https://dmtclearinghouse.esipfed.org">Data management Training Clearinghouse</a> (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: <a href="https://imls.gov/grants/awarded/lg-70-18-0092-18">LG-70-18-0092-18</a>) <em>Development of an Enhanced and Expanded Data Management Training Clearinghouse project.</em> The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.</p> <p>The materials included in the collection consist of the following:</p> <ul> <li><strong>esip-dev-02.edacnm.org.json.zip</strong> - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.</li> <li><strong>statistics.pdf</strong> - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, <strong>and</strong> preliminary metadata for unpublished learning resources that are not included in the shared dataset. </li> </ul> <p>The metadata fields consist of the following:</p> <table> <thead> <tr> <th scope="col">Fieldname</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>abstract_data</td> <td>A brief synopsis or abstract about the learning resource</td> </tr> <tr> <td>abstract_format</td> <td>Declaration for how the abstract description will be represented.</td> </tr> <tr> <td>access_conditions</td> <td>Conditions upon which the resource can be accessed beyond cost, e.g., login required.</td> </tr> <tr> <td>access_cost</td> <td>Yes or No choice stating whether othere is a fee for access to or use of the resource.</td> </tr> <tr> <td>accessibililty_features_name</td> <td>Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.</td> </tr> <tr> <td>accessibililty_summary</td> <td>A human-readable summary of specific accessibility features or deficiencies.</td> </tr> <tr> <td>author_names</td> <td>List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system</td> </tr> <tr> <td>author_org<br> - name<br> - name_identifier<br> - name_identifier_type</td> <td> <p><br> - Name of organization authoring the learning resource.<br> - The unique identifier for the organization authoring the resource.<br> - The identifier scheme associated with the unique identifier for the organization authoring the resource.</p> </td> </tr> <tr> <td> <p>authors<br> - givenName<br> - familyName<br> - name_identifier<br> - name_identifier_type</p> </td> <td> <p><br> - Given or first name of person(s) authoring the resource.<br> - Last or family name of person(s) authoring the resource.<br> - The unique identifier for the person(s) authoring the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.</p> </td> </tr> <tr> <td>citation</td> <td>Preferred Form of Citation.</td> </tr> <tr> <td>completion_time</td> <td>Intended Time to Complete</td> </tr> <tr> <td> <p>contact<br> - name<br> - org<br> - email</p> </td> <td> <p><br> - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - (excluded) Contact email address.</p> </td> </tr> <tr> <td>contributor_orgs<br> - name<br> - name_identifier<br> - name_identifier_type<br> - type</td> <td>- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.<br> - The unique identifier for the organization contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the organization contributing to the resource.<br> - Type of contribution to the resource made by an organization.</td> </tr> <tr> <td>contributors<br> - familyName<br> - givenName<br> - name_identifier<br> - name_identifier_type</td> <td> <p>- Last or family name of person(s) contributing to the resource.<br> - Given or first name of person(s) contributing to the resource.<br> - The unique identifier for the person(s) contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.</p> </td> </tr> <tr> <td> <p>contributors.type</p> </td> <td> <p>Type of contribution to the resource made by a person.</p> </td> </tr> <tr> <td>created</td> <td>The date on which the metadata record was first saved as part of the input workflow.</td> </tr> <tr> <td>creator</td> <td>The name of the person creating the MD record for a resource.</td> </tr> <tr> <td>credential_status</td> <td>Declaration of whether a credential is offered for comopletion of the resource.</td> </tr> <tr> <td> <p>ed_frameworks<br> - name<br> - description<br> - nodes.name</p> </td> <td>- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.<br> - A description of one or more subcategories of an educational framework to which a resource is associated.<br> - The name of a subcategory of an educational framework to which a resource is associated.</td> </tr> <tr> <td>expertise_level</td> <td>The skill level targeted for the topic being taught.</td> </tr> <tr> <td>id</td> <td>Unique identifier for the MD record generated by the system in UUID format.</td> </tr> <tr> <td>keywords</td> <td>Important phrases or words used to describe the resource.</td> </tr> <tr> <td>language_primary</td> <td>Original language in which the learning resource being described is published or made available.</td> </tr> <tr> <td>languages_secondary</td> <td>Additional languages in which the resource is tranlated or made available, if any.</td> </tr> <tr> <td>license</td> <td>A license for use of that applies to the resource, typically indicated by URL.</td> </tr> <tr> <td>locator_data</td> <td>The identifier for the learning resource used as part of a citation, if available.</td> </tr> <tr> <td>locator_type</td> <td>Designation of citation locatorr type, e.g., DOI, ARK, Handle.</td> </tr> <tr> <td>lr_outcomes</td> <td>Descriptions of what knowledge, skills or abilities students should learn from the resource.</td> </tr> <tr> <td>lr_type</td> <td>A characteristic that describes the predominant type or kind of learning resource.</td> </tr> <tr> <td>media_type</td> <td>Media type of resource.</td> </tr> <tr> <td>modification_date</td> <td>System generated date and time when MD record is modified.</td> </tr> <tr> <td>notes</td> <td>MD Record Input Notes</td> </tr> <tr> <td>pub_status</td> <td>Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.</td> </tr> <tr> <td>published</td> <td>Date of first broadcast / publication.</td> </tr> <tr> <td>publisher</td> <td>The organization credited with publishing or broadcasting the resource.</td> </tr> <tr> <td>purpose</td> <td>The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.</td> </tr> <tr> <td>rating</td> <td>The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>ratings</td> <td>Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>resource_modification_date</td> <td>Date in which the resource has last been modified from the original published or broadcast version.</td> </tr> <tr> <td>status</td> <td>System generated publication status of the resource w/in the registry as a yes for published or no for not published.</td> </tr> <tr> <td>subject</td> <td>Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.</td> </tr> <tr> <td>submitter_email</td> <td>(excluded) Email address of person who submitted the resource.</td> </tr> <tr> <td>submitter_name</td> <td>Submission Contact Person</td> </tr> <tr> <td>target_audience</td> <td>Audience(s) for which the resource is intended.</td> </tr> <tr> <td>title</td> <td>The name of the resource.</td> </tr> <tr> <td>url</td> <td>URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.</td> </tr> <tr> <td>usage_info</td> <td>Descriptive information about using the resource, not addressed by the License information field.</td> </tr> <tr> <td>version</td> <td>The specific version of the resource, if declared.</td> </tr> </tbody> </table> <p> </p>
Coronavirus Twitter Data: A collection of COVID-19 tweets with automated annotations
<p>This dataset contains tweets related to COVID-19. The dataset contains Twitter ids, from which you can download the original data directly from Twitter. Additionally, we include the date, keywords related to COVID-19 and the inferred geolocation. Check detailed information at <a href="http://twitterdata.covid19dataresources.org/index">http://twitterdata.covid19dataresources.org/index</a>.</p>
Data and code associated with the paper 'Measurement-induced collective vibrational quantum coherence under spontaneous Raman scattering in a liquid'
<p>Data and code associated with the following paper <a href="https://doi.org/10.1038/s41467-023-38483-9">V. Vento, S. Tarrago-Velez et al., Nat. Commun. (2023)</a></p> <p>A thorough explanation of the experiment performed is available there.</p> <p>The name of each sub-folder and file in <strong>CS2_data_code.zip</strong> indicates the corresponding figure number ("FIG #") and the type of content ("Data", "Analysis" or "Model").</p> <p> </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.