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Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI)
<pre><a href="http://data.pymvpa.org/datasets/haxby2001/">http://data.pymvpa.org/datasets/haxby2001/</a> This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references_ below. Terms Of Use ============ The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/</pre> <pre>References ========== :ref:`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001) <HGF+01>`. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. :ref:`Hanson, S., Matsuka, T., and Haxby, J. (2004) <HMH04>`. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166. :ref:`O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005) <OJA+05>`. Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. :ref:`Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009) <HHS+09b>`. PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.</pre> <p> </p>
Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal faces".
<p>Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal facets".</p> <p>David John1,2, Zdeněk Remeš1, Radim Novák1, Štěpán Remeš1, Jakub Volf1,3,4, Oleg Babčenko1, Egor Ukraintsev5, Bohuslav Rezek5, and Maksym Buryi3</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10/112, 162 00, Prague, Czech Republic<br>2 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University, Břehová 7, 115019, Prague, Czech Republic<br>3 Institute of Plasma Physics of the Czech Academy of Sciences, U Slovanky 2525/1a, 182 00, Prague, Czech Republic <br>4 Department of Inorganic Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28, Czech Republic<br>5 Faculty of Electrical Engineering of the Czech Technical University, Technická 2, 160 00, Prague, Czech Republic</p> <p> </p> <p>Dataset description:</p> <p>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00000 AFM data<br>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00003 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00001 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00003 AFM data<br>afm zn AFM data<br>data phase shift fit phase shift data<br>grafy phase shift fit phase shift data<br>ZnO faces optical images</p>
Beach-face slope dataset for Australia
<p>This repository contains a dataset of beach-face slopes for the Australian coastline. It includes more than 13,200 km of sandy coast with estimates of the beach-face slopes provided every 100 m. The methodology and dataset are described in:</p> <p><em>Vos, K., Deng, W., Harley, M. D., Turner, I. L., and Splinter, K. D. M.: Beach-face slope dataset for Australia, Earth Syst. Sci. Data, 14, 1345–1357, https://doi.org/10.5194/essd-14-1345-2022, 2022.</em></p> <p>The beach-face slope data is provided in 2 separate GEOJSON files: <strong>Australia_slopes_by_transect.geojson</strong> and <strong>Australia_slopes_by_beach.geojson</strong>. The first one presents the data along each transect (total of 132,132 beach transects) and the second one presents the data for each individual beach/embayment (total of 5,207 beaches). Additionally, there are three layers that contain polygons for the Australian coastal regions, primary compartments and secondary compartments, respectively.</p> <p>The coordinate system for the geospatial layers is WGS84.</p> <p><strong>1. Australia_slope_by_transect.geojson</strong>: contains a geospatial layer with cross-shore transects along the Australian sandy coastline. Each feature in this layer is a transect (2 point linestring) with the following attributes:<br> - <em>transect_id</em>: Database id for each transect, e.g., aus0001-0000, aus0001-0001, …<br> - <em>beach_id</em>: Database id for each beach, e.g., aus0001, aus0002, …, aus5255<br> - <em>beach_slope</em>: estimate of the beach-face slope between Mean Sea Level (MSL) and Mean High Water Springs (MHWS), value between 0.01 and 0.2<br> - <em>lower_conf_bound</em>: Lower limit of the confidence band for the slope estimate<br> - <em>upper_conf_bound</em>: Upper limit of the confidence band for the slope estimate<br> - <em>width_conf_band</em>: Width of confidence band, value between 0 and 0.19)<br> - <em>sl_points</em>: Number of datapoints in the shoreline time-series used for beach-face slope estimation (minimum set to 100)<br> - <em>quality_flag</em>: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)<br> - <em>coastal_region</em>: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)<br> - <em>primary_comp_id</em>: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)<br> - <em>secondary_comp_id</em>: Database id corresponding to the 361 secondary sediment Compartments as identified by Thom et al. (2018)</p> <p><strong>2. Australia_slope_by_beach.geojson</strong>: contains a geospatial layer with each individual beach/embayment (as a linestring) along the Australian sandy coastline. Each feature has the following attributes:<br> - <em>beach_id</em>: Database id for each beach, e.g., aus0001, aus0002, …, aus5255<br> - <em>beach_slope_average</em>: Average of the beach-face slope at the site, weighted by the width of the confidence bands, value between 0.01 and 0.2<br> - <em>width_ci_average</em>: Average width of confidence band over the comprised transects, value between 0 and 0.19<br> - <em>quality_flag</em>: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)<br> - <em>mstr</em>: Mean Spring Tide Range at the beach calculated from the closest grid point in the FES2014 global tide model<br> - <em>hsig_median</em>: Median Significant Wave Height from the closest grid point in the CAWCR re-analysis dataset<br> - <em>prc_msrt_obs</em>: percentage of the Mean Spring Tide Range observed by the satellite-derived shorelines<br> - <em>min_tide_obs</em>: Lowest tide level observed by the satellite-derived shorelines<br> - <em>max_tide_obs</em>: Highest tide level observed by the satellite-derived shorelines<br> - <em>sl_points_average</em>: Average number of datapoints in the shoreline time-series over the comprised transects<br> - <em>beach_length</em>: Length of the beach or embayment, very long beaches (>50km) were split to optimise memory usage when downloading the satellite images<br> - <em>coastal_region</em>: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)<br> - <em>primary_comp_id</em>: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)<br> - <em>secondary_comp_id</em>: Database id corresponding to the 361 secondary sediment Compartments as identified by Thom et al. (2018)</p> <p><br> In addition to these two layers, the 3 different levels of the Sediment Compartments framework (Thom et al.. 2018) with their average beach-face slopes are also included here.</p> <p><strong>3. coastal_regions.geojson</strong>: contains a geospatial layer of each coastal region (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:<br> - <em>name</em>: name of the coastal region, e.g., Pilbara, Kimberley, etc<br> - <em>beach_slope_average_by_beach</em>: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon<br> - <em>beach_slope_average_by_transect</em>: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon</p> <p><strong>4. primary_compartments.geojson</strong>: contains a geospatial layer of each primary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:<br> - <em>primary_comp_id</em>: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)<br> - <em>name</em>: name of each primary sediment compartment<br> - <em>beach_slope_average_by_beach</em>: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon<br> - <em>beach_slope_average_by_transect</em>: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon</p> <p><strong>5. secondary_compartments.geojson</strong>: contains a geospatial layer of each secondary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:<br> - <em>secondary_comp_id</em>: Database id corresponding to the 361 secondary sediment compartments as identified by Thom et al. (2018)<br> - <em>name</em>: name of each secondary sediment compartment<br> - <em>beach_slope_average_by_beach</em>: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon<br> - <em>beach_slope_average_by_transect</em>: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon</p>
Hugging Face Models Dataset
<p>This dataset contains meta-data of all models published in Hugging Face. The purpose of the dataset is to allow people analyse the state-of-the-art of ML models in a wide variety of ways.</p> <p>The dataset was generated in 2022-11-20 by web scraping the Hugging Face Models Hub. At this moment, there are 89919 models and there were captured 15 different attributes.</p> <p> </p>
Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts
<p>Datasets for Experiment 1 and Experiment 2 of "Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts"</p>
Nevada Desert FACE Facility Soil Organic Carbon Data
This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.
Proteomic analysis reveals different molecular mechanisms to face water deficit in mycorrhizal and nonmycorrhizal sorghum plants
<p>Differential accumulated proteins in response to water deficit in mycorrhizal and nonmycorrhizal sorghum plants were recovered from 2D gels and identified by HPLC-MSMS. MS analysis was performed by a Nano acquity nanoflow LC system (Waters, Milford, MA, USA) coupled to a linear ion trap (LTQ) velos mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a nanoelectrospray ion source.</p>
PsPM-trSP4: SCR measurement in response to face photographs withangry, neutral, and fearful expression while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 42 healthy unmedicated participants (21 males and 21 females aged 25.2 +/- 4.0 years) in response to 38 face photographs (modified from the Karolinska Directed Emotional Faces set, KDEF), each presented once with angry, neutral, and fearful expression for 1 s each. Meanwhile, participants were listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.5 s, 9.0 s, or 10.5 s (misprinted in the publications), plus a variable delay of around 0.1 s for image loading. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the SCR recordings.</p>
Beach-face slopes from satellite-derived shorelines along SE Australia and California
<p>This repository contains the data described in Vos, K., Harley, M. D., Splinter, K. D., Walker, A., & Turner, I. L. (2020). Beach Slopes From Satellite‐Derived Shorelines. <em>Geophysical Research Letters</em>, <em>47</em>(14), e2020GL088365.</p> <p>There are 2 GEOJSON files in this repository. The coordinate system for both geospatial layers is WGS84 (epsg:4326).</p> <p>1. <strong>slopes_along_transects.geojson</strong>: contains a geospatial layer with cross-shore transects for sandy coastlines along SE Australia and California. Each feature in this layer is a transect (2 point linestring) with the following attributes:<br> - <strong>site_id</strong>: id of the beach in which the transect is located<br> - <strong>id</strong>: id of the individual transect<br> - <strong>orientation</strong>: orientation of the transect in degrees from North (positive clockwise)<br> - <strong>beach slope</strong>: beach-face slope from Mean Sea Level (MSL) to Mean High Water Springs (MHWS)</p> <p><br> 2. <strong>slopes_along_beaches.geojson</strong>: this layer contains sandy beaches instead of transects. For each beach the median slope has been calculated from all the available transects. The following attributes are available:<br> - <strong>id</strong>: id of the beach<br> - <strong>name</strong>: name of the beach in the OpenStreetMap database (if not available 'noname')<br> - <strong>beach_length</strong>: length of the beach in metres<br> - <strong>median_orientation</strong>: beach orientation calculated as the median of the orientations of each transect<br> - <strong>Tide range</strong>: average tidal range (mean high water - mean low water) at each beach based on FES2014 global tide model<br> - <strong>median_slope</strong>: median beach-face slope along the beach based on the estimated beach-face slope along the transects</p> <p> </p>
Fight or flight? Behaviour and experiences of laypersons in the face of an incipient fire
<p>This dataset contains raw data collected in an experimental study by the University of Muenster, Germany, in cooperation with the German Fire Protection Association and the State Fire Service Institute NRW, Germany. The study is part of a larger research project and examined the behavior of laypersons when confronted with an incipient fire.</p> <p>Within minutes, an incipient fire can develop into a life-threatening full fire. Consequently, it should be fought as early as possible. But are laypersons capable of doing this? In such a situation, how do they behave and feel? These questions are addressed in the current study. Persons without any professional firefighting training (N=64) were confronted in two experimental runs with a real incipient fire in the form of a burning pillow.</p> <p>The study was approved by the ethics committee of the Department 7 of the University of Münster (ID 2018-16-MT) and pre-registered with AsPredicted.org under the number 20436 (https://aspredicted.org/8py33.pdf). The study was supported by the German Federal Ministry of Education and Research (funding code FKZ 13N14208).</p> <p>In addition to the raw data, the codebook as well as the R- analysis script is included here for better comprehensibility. The raw data contains only the information of persons who were included in the analysis and have agreed to it. Some demographic information was deleted to ensure anonymity.</p>
Kashmir (?). Stamp with a pierced handle with small volutes and curved face, incised with an inscription.
<p>Kashmir (?). Stamp with a pierced handle with small volutes and curved face, incised with the <em>ye dharmā</em> formula, <em>circa</em> seventh century (h. 3.5 centimetres). Collected in the Punjab by <a href="https://research.britishmuseum.org/research/collection_online/collection_search_results.aspx?people=136295&peoA=136295-3-31">Jean-Baptiste Ventura</a>. Acquired by the India Museum, London, and subsequently transferred to the British Museum in 1880. Registered as 1880.167 in the British Museum. The photograph of the inscription proper is reversed to show the letters as they would appear in an impression.</p>
- Fore wing with areolet open (a); body and wings colorations variable but usually wings hyaline and body yellow interspersed with black markings (b) …………………………………2 (Epirhyssa) 2. Face smooth to sparsely punctate (A-B), without transverse rugosities ……………………………3 - Face transversely rugulose punctate or transversely striate (a-b) ……………………………………5 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Fore wing with areolet open (a); body and wings colorations variable but usually wings hyaline and body yellow interspersed with black markings (b) …………………………………2 (Epirhyssa) 2. Face smooth to sparsely punctate (A-B), without transverse rugosities ……………………………3 - Face transversely rugulose punctate or transversely striate (a-b) ……………………………………5
Multi-Attributed Structured Text-to-face Dataset
<p>A new data consolidation called Multi-Attributed and Structured Text-to-face (MAST) dataset. The motivation is to have a large corpus of high-quality face images with fine-grained and attribute-focussed annotations. This has the benefits of the attribute oriented approach as well as the semantics in a textual description.</p>
Fig. 96. T1 anterior face. A in The 'red-tailed' Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic
Fig. 96. T1 anterior face. A. Lasioglossum (D.) testaceum (Robertson, 1897), ♀, acarinarial fan absent with erect hairs throughout. B. L. (D.) clastipedion sp. nov., ♀, acarinarial fan present but weak, with erect hairs restricted to lateral margins. Scale bars: 0.5 mm.
Fig. 95. T1 anterior face. A in The 'red-tailed' Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic
Fig. 95. T1 anterior face. A. Lasioglossum (D.) kunzei (Cockerell, 1898), ♀, shiny with weak and worn acarinarial fan. B. L. (D.) argammon sp. nov., ♀, weakly coriarious with sparse acarinarial fan. C. L. (D.) rufornatum sp. nov., ♀, strongly coriarious with dense and well-developed acarinarial fan. Arrows point to areas where microsculpture is most easily visible. Scale bar is 0.5 mm.
MAPIR Faces Dataset
<p>The MAPIR Faces Dataset (publication still pending) is a collection of face images from 16 different individuals. Each individual has approximately 49 images uniformly distributed in a 7x7 grid over the head pose space defined by:</p> <ul> <li>Pitch <span class="math-tex">\(\in (-65.0,~65.0)\)</span></li> <li>Yaw <span class="math-tex">\(\in (-35.0,~35.0)\)</span></li> </ul> <p>This dataset is designed as a benchmark to analyze the effect of detrimental factors due to pose variance in face recognition algorithms.</p>
Participant Faces from a Repeated Prisoner's Dilemma
<p>The authors (ES and TS) conducted a computerized laboratory experiment in an experimental economics laboratory using a modified Prisoner’s Dilemma known as <em>Friend </em>or <em>Foe </em>(e.g. see List, 2006. <em>The Review of Economics and Statistics</em>, <em>88</em>(3), 463-471). 96 young adults aged 18 to 25 years old (51 men, 45 women) gave permission to be video recorded at intervals throughout the experimental procedure under standardized videographic conditions and for their recordings and experiment data to be made available for later research. All individuals video recorded were students at Chapman University in Orange, CA. We provide a dataset described below based on behavioral game and survey data collected from participants over a computer network, media from video recordings, and media analysis measures.</p> <p><em><strong>Description of Experimental Task:</strong> </em>Participants were provided a printed copy of the instructions (Instructions.pdf) explaining the following experimental procedure. Participants were randomly paired with one other person, with whom they anonymously interacted over a computer network throughout the experimental session. Participants were told that at no time would their or other participants’ true identity or videorecorded images be revealed to participants in the experiment session. Participants were told to expect a minimum of two rounds of game interactions with possibility for more rounds. Each round required the participants to first privately state their (non-binding) intention for the camera, saying either “I intend to Split” or “I intend to Take All” (which we videorecorded, see <em>Media: Videos of Stated Intent</em>), then to choose an interaction strategy (either “Split” or “Take All”). Each round we provided participants the results of their pair’s game interaction on their computer screens. We videorecorded participants discovery, viewing, and reaction to the interaction results (see <em>Media: Videos of Viewed Results</em>). After the last round of interaction, we collected response data using an exit survey.</p> <p><strong><em>Game and Survey Data</em>:</strong> Data includes participants’ stated intent and game behavior for each round, emotional reactions (each on a scale of 1 to 5) self-reported after learning the results of each round (measured with a modified 20 item PANAS), and the following data collected during an exit survey: responses (each on a scale of 0 to 4) to six questions used for scoring the Revised Life Orientation Test (from Scheier et al., 1994. <em>Journal of Personality and Social Psychology, 67</em>, 1063-1078), age, gender, first language, and English language fluency.</p> <p><strong><em>Media:</em></strong> Videos of participants were taken using computer display mounted Logitech C920 1080P HD digital cameras, at computer terminals in individual cubicles. The cameras were aimed at participants who sat against a background of either a gray carpeted cubicle or a brown wall under standardized diffuse lighting conditions. For video recordings in this data set, participants were instructed to wear microphone-earphone headsets, to keep hair away from their face, and to sit upright and look at the computer screen they were facing. Camera-to-head distance was controlled by the cubicle space and chair position, and factory default camera settings were held constant.</p> <p><em>Video of Stated Intent.</em> 204 eight-second videos with audio were taken after an on-screen prompt instructed participants to make a spoken non-binding statement of intent. During this phase, we played grey noise over a public address system that masked ambient sound from beyond the individual cubicle within which the video recording equipment was situated. In combination with well insulated over-ear headphones worn by participants, the grey noise was helpful for preventing interference or leakage of other participants’ statements.</p> <p><em>Video of Viewed Results</em>. 204 eight-second videos with audio were taken that capture the moment of participants discovery, viewing, and reaction to the interaction results. The interaction results appear and remain visible to participants on their computer screens from around 2.5 seconds (no earlier than 2 seconds) into the 8 second videos.</p> <p><em>Thin Slice Video.</em> 204 videos (two to three seconds in length) without audio were trimmed from full <em>Videos of Stated Intent</em>. These thin slice videos capture the 2-3 second interval directly following the concluded stated intent.</p> <p><em>Photograph</em>. 204 photographs (640 x 480 pixel .jpg files, 24 Bit depth) including a participant’s full face were captured from video using the VLC media player (3.0.11) “snapshot” tool.</p> <p><strong><em>Media Analysis Measures:</em></strong></p> <p><em>Stated Intent</em>. The particular statement of intent (either “I intend to Split” or “I intend to Take All”) unambiguously recognized by the authors upon inspection of the sound and appearance of each <em>Video of Stated Intent’s</em> recorded statement, is noted in the attached Media.csv file.</p> <p><em>Skin Coloration in Photographs</em>. Using Adobe Photoshop 2020, skin patches (51 × 51 pixels) were selected from the left and right cheeks of each <em>photograph </em>face image. These cheek areas were selected so that they included only skin surfaces but never the edge of a face, ears, lips, or eyelashes. The light (L*),red (a*), and yellow(b*) values that are relevant to human color perception were then measured by centering a "color sampler tool" in Photoshop that averaged over a 51x51 pixel area the CIELab color space values. L*,a*,b* values for right and left cheeks in <em>Photographs</em> are noted in the attached Media.csv file.</p> <p><em>Facial Width to Height in Photographs</em>: To measure facial width to height ratio (fWHR) in photographs, we used tools in Photoshop 2020: first we rotated the photos as necessary using image rotation so that pupils were levelled horizontally, we record the degree of positive or negative rotation used, then we used the <em>frame tool </em>to measure the upperface “height” distance between the upper edge of upper lip and lower edge of brows and the “width” distance between left and right zygion (byzygomatic width) of face images. These methods are consistent with Carre and McCormick (2008, <em>Proceedings of the Royal Society B</em><em>‐</em><em> Biological Sciences, 275</em>, 2651–2656.) methods for measuring the height of upperface and byzygomatic width of face images. The facial width and height in <em>Photographs</em> are noted in the attached Media.csv file.</p> <p><strong>Data and files: </strong>We provide audio-video and image files of <em>Media </em>along with the open-access .csv files detailed below<em>. </em>The above data is organized by the following three data structures in .csv file format.</p> <p>Fields.csv<strong> </strong>is an open-access file includes <em>Game and Survey Data</em> organized by participants and rounds in the experiment. Fields_codebook.csv is a codebook explaining the column headers and participants’ data in the Fields.csv file.</p> <p>Media.csv is an open-access file that catalogues the names and types of <em>Media </em>files provided and includes <em>Media Analysis Measures</em>. Media_codebook.csv is a codebook explaining column headers and participants’ data in the Media.csv file.</p> <p>MappingFile.csv<strong> </strong>is a restricted file linking the participant data in the Fields.csv and Media.csv files. Please contact the authors for this access to this key at <a href="http://doi.org/10.5281/zenodo.4321814">http://doi.org/10.5281/zenodo.4321814</a> if you plan to work at the intersection of these data or replicate published work based on these data.</p> <p>Instructions.pdf is the instructions document provided to all participants in the experiment from which the above data was collected.</p>
FIGURE 2. Bothriomyrmex paradoxus queen. a. Face view, holotype. b. Lateral view, holotype. c in A new species of the genus Bothriomyrmex Emery, 1869 (Hymenoptera: Formicidae: Dolichoderinae) from Costa Rica
FIGURE 2. Bothriomyrmex paradoxus queen. a. Face view, holotype. b. Lateral view, holotype. c. Wing, paratype.
PROCRAFT Final Meeting - The historian facing aeronautical heritage or historical objectivity facing mythology by Jean-Marc Olivier
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Cows Frontal Face Dataset
<p>The dataset is one of the biggest dataset of cows having 459 classes in total. It can be utilized for various purposes related to cow identification. Our work and dataset is limited to Muzzle Detection. The recognition part will be exploring in future using the noise removal and deep learning techniques in the next versions utilizing the same dataset. A limited number of data sets are accessible for analyzing cow muzzle, with only two publicly available datasets from Australia and United States. Additionally, there is no existing method for identifying cows using AI and computer vision, which means that there are no real-time photos available for training a model. The majority of the dataset used in our research has been collected by our team. We have collected the world’s largest dataset in number of subjects in Pakistan.</p>
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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.