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1,478 results for “face”
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
<p>Data supporting "Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News". <br><br></p> <p> If you use this dataset in your own research, please cite this paper:</p> <p>```<br>@misc{abels2024mitigating,<br> title={Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News}, <br> author={Axel Abels and Elias Fernandez Domingos and Ann Nowé and Tom Lenaerts},<br> year={2024},<br> eprint={2403.08829},<br> archivePrefix={arXiv},<br> primaryClass={cs.HC}<br>}<br>```</p> <p> </p> <table> <tbody> <tr> <td><strong>column name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>treatment</td> <td>identifier for the set of headlines presented to the participant</td> </tr> <tr> <td>trial</td> <td>trial/round in which the headline was presented </td> </tr> <tr> <td>arm</td> <td>which "arm" the headline was presented as (0=left, 1=middle, 2=right)</td> </tr> <tr> <td>advice</td> <td>the participant's response (0=very unlikely, 0.25=unlikely, 0.5=undecided, 0.75=likely, 1=very likely)</td> </tr> <tr> <td>genuine</td> <td>whether the headline was genuine (1) or altered (0)</td> </tr> <tr> <td>headline</td> <td>the headline as shown to the participant</td> </tr> <tr> <td>original</td> <td>the headline before a possible alteration</td> </tr> <tr> <td>expert_id</td> <td>participant's identifier</td> </tr> <tr> <td>sentiment</td> <td>whether the headline reported a negative (-1) or positive (1) outcome</td> </tr> <tr> <td>expert:ethnicity</td> <td>the participant's ethnicity</td> </tr> <tr> <td>expert:sex</td> <td>the participant's sex</td> </tr> <tr> <td>expert:age</td> <td>the participant's age</td> </tr> <tr> <td>outcome:white, outcome:black, outcome:young, outcome:old, outcome:male, outcome:female</td> <td>whether the headline reported a negative (-1) or positive (1) or neutral (0) outcome for the specified group</td> </tr> <tr> <td>trial_time</td> <td>how long the participant took to respond to the trial/round</td> </tr> </tbody> </table> <p><strong>abstract</strong><br>Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and their permeation into collective decisions. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment. We then use our collected data as a benchmark problem on which we evaluate the efficacy of adaptive aggregation algorithms. In addition to their improved accuracy, our results highlight the interactions between the emergence of collective intelligence and the mitigation of participant biases. </p>
refering rawdata and code of "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)"
<p>Corresponding raw data and codes that produce all relative video and imaging results for real-time monitoring the physicochemical phenomena of microfluidics, microorganism's group, and chemical reactions, supporting and verifying the research article "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)".</p>
Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset
<p>Extension of the FFHQ dataset (https://github.com/NVlabs/ffhq-dataset) with precise bounding box annotations for eyeglasses detection.</p> <p>If you find this dataset useful in your research, please consider citing the original dataset and the following papers:</p> <p>@article{matuzevicius2024diverse,<br> title={Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Sensors},<br> volume={24},<br> number={23},<br> pages={7697},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/s24237697}<br>}</p> <p>@article{matuzevicius2024retrospective,<br> title={A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Electronics},<br> volume={13},<br> number={23},<br> pages={4763},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/electronics13234763}<br>}</p> <p> </p>
Fig. 16. Females, face. A. Scolia binotata Fabricius, 1804. B in A review of the digger wasps (Insecta: Hymenoptera: Scoliidae) of Hong Kong, with description of one new species and a key to known species
Fig. 16. Females, face. A. Scolia binotata Fabricius, 1804. B. Sc. binotata orange var. C. Sc. clypeata pseudovollenhoveni Betrem, 1933. D. Sc. pakshaoensis sp. nov., holotype (CAS). E. Sc. superciliaris de Saussure & Sichel, 1864.
Fig. 17. Males, face. A in A review of the digger wasps (Insecta: Hymenoptera: Scoliidae) of Hong Kong, with description of one new species and a key to known species
Fig. 17. Males, face. A. Campsomeriella annulata annulata (Fabricius, 1793). B. Camps. collaris (Fabricius, 1775). C. Megacampsomeris sp. 1. D. Megacam. formosensis chinensis Betrem, 1941. E. Megacam. prismatica (Smith, 1855). F. Phalerimeris phalerata phalerata (de Saussure, 1858). G. Sericocampsomeris flavomaculata Gupta & Jonathan, 1989.
Fig. 15. Females, face. A in A review of the digger wasps (Insecta: Hymenoptera: Scoliidae) of Hong Kong, with description of one new species and a key to known species
Fig. 15. Females, face. A. Campsomeriella annulata annulata (Fabricius, 1793). B. Camps. collaris (Fabricius, 1775). C. Megacampsomeris formosensis chinensis Betrem, 1941. D. Phalerimeris phalerata phalerata (de Saussure, 1858). E. Austroscolia ruficeps ruficeps (Smith, 1855). F. Carinoscolia junnanensis (Betrem, 1928). G. Liacos erythrosoma (Burmeister, 1854). H. Megascolia azurea (Christ, 1791).
Fig. 18. Males, face. A in A review of the digger wasps (Insecta: Hymenoptera: Scoliidae) of Hong Kong, with description of one new species and a key to known species
Fig. 18. Males, face. A. Carinoscolia junnanensis (Betrem, 1928). B. Liacos erythrosoma (Burmeister, 1854). C. Megascolia azurea (Christ, 1791). D. Scolia binotata Fabricius, 1804. E. Sc. laeviceps Smith, 1855. F. Sc. pakshaoensis sp. nov., paratype (CBC). G. Sc. superciliaris de Saussure & Sichel, 1864.
NII Face Mask Dataset
<p>=====================================================================<br> # NII Face Mask Dataset v1.0<br> =====================================================================</p> <p>Authors:<br> Trung-Nghia Le (1), Khanh-Duy Nguyen (2), Huy H. Nguyen (1), Junichi Yamagishi (1), Isao Echizen (1)</p> <p>Affiliations:<br> (1)National Institute of Informatics, Japan <br> (2)University of Information Technology-VNUHCM, Vietnam</p> <p>National Institute of Informatics <br> Copyright (c) 2021</p> <p>Emails:<br> {ltnghia, nhhuy, jyamagis, iechizen}@nii.ac.jp, {khanhd}@uit.edu.vn</p> <p>Arxiv: https://arxiv.org/abs/2111.12888<br> NII Face Mask Dataset v1.0: https://zenodo.org/record/5761725</p> <p>=============================== INTRODUCTION ===============================</p> <p>The NII Face Mask Dataset is the first large-scale dataset targeting mask-wearing ratio estimation in street cameras. This dataset contains 581,108 face annotations extracted from 18,088 video frames (1920x1080 pixels) in 17 street-view videos obtained from the Rambalac's YouTube channel.</p> <p>- https://www.youtube.com/c/Rambalac</p> <p>The videos were taken in multiple places, at various times, before and during the COVID-19 pandemic. The total length of the videos is approximately 56 hours.</p> <p><br> =============================== REFERENCES ===============================</p> <p>If your publish using any of the data in this dataset please cite the following papers:</p> <p>#Pre-print version<br> @article{Nguyen202112888,<br> title={Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio},<br> author={Nguyen, Khanh-Duy and Nguyen, Huy H and Le, Trung-Nghia and Yamagishi, Junichi and Echizen, Isao},<br> archivePrefix={arXiv},<br> arxivId={2111.12888},<br> url={https://arxiv.org/abs/2111.12888},<br> year={2021}<br> }</p> <p>#Final version<br> @INPROCEEDINGS{Nguyen2021EstMaskWearing,<br> author={Nguyen, Khanh-Duv and Nguyen, Huv H. and Le, Trung-Nghia and Yamagishi, Junichi and Echizen, Isao},<br> booktitle={2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)}, <br> title={Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio}, <br> year={2021},<br> pages={1-8},<br> url={https://ieeexplore.ieee.org/document/9667046},<br> doi={10.1109/FG52635.2021.9667046}}</p> <p><br> ======================== DATA STRUCTURE ==================================</p> <p><br> 1. Directory Structure<br> -------------------------------</p> <p>./NFM<br> ├── dataset<br> │ ├── train.csv: annotations for the train set.<br> │ ├── test.csv: annotations for the test set.<br> └── README_v1.0.md</p> <p><br> 2. Description for each files in detail.<br> ---------------------------------------------------------</p> <p>We use the same structure for two CSV files (train.csv and test.csv). Both CSV files have the same columns:<br> <1st column>: video_id (a source video can be found by following the link: https://www.youtube.com/watch?v=<video_id>)<br> <2nd column>: frame_id (the index of a frame extracted from the source video)<br> <3rd column>: timestamp in milisecond (the timestamp of a frame extracted from the source video)<br> <4th column>: label (for each annotated face, one of three labels was attached with a bounding box: 'Mask'/'No-Mask'/'Unknown')<br> <5th column>: left<br> <6th column>: top<br> <7th column>: right<br> <8th column>: bottom<br> Four coordinates (left, top, right, bottom) were used to denote a face's bounding box. </p> <p><br> ============================== COPYING ================================</p> <p>This repository is made available under Creative Commons Attribution License (CC-BY). </p> <p>Regarding Creative Commons License: Attribution 4.0 International (CC BY 4.0), <br> please see https://creativecommons.org/licenses/by/4.0/</p> <p>THIS DATABASE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND <br> ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED <br> WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. <br> IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, <br> INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, <br> BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, <br> OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, <br> WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) <br> ARISING IN ANY WAY OUT OF THE USE OF THIS DATABASE, EVEN IF ADVISED OF THE <br> POSSIBILITY OF SUCH DAMAGE</p> <p><br> ====================== ACKNOWLEDGEMENTS ================================</p> <p>This research was partly supported by JSPS KAKENHI Grants (JP16H06302, JP18H04120, JP21H04907, JP20K23355, JP21K18023), and JST CREST Grants (JPMJCR20D3, JPMJCR18A6), Japan.</p> <p>This dataset is based on the Rambalac's YouTube channel: https://www.youtube.com/c/Rambalac<br> </p>
WageIndicator salary survey face-to-face 2006-2013 merged data
<p>The dataset includes the merged data of face-to-face salary surveys in 28 countries, undertaken between 2006 and 2013. The total number of observations is 59,138 individuals in the labour force. Almost all countries are developing countries in Africa, Asia, and Latin America.</p>
बोधगया, बिहार. Terracotta plaque with old Mon inscription, rear face.
<p>बोधगया, बिहार. Terracotta plaque with old Mon inscription, rear face. British Museum 1887,0717.82. © ब्रिटिश म्यूजियम</p>
Raw data for Facemasks and face recognition: Potential impact on synaptic plasticity
<p>Figure 2 legend Upper panel. In control condition, visual sensory inputs from in- dividual’s face are encoded by the face recognition system. At system level (a), this process implies functional and structural modifications in multiple brain regions, whereas at cellular level (b), this promotes the induction of distinct forms of synaptic plasticity, such as long-term potentiation and long-term depression (LTP, LTD, respectively). Lower panel. Wearing face masks consis- tently reduces the amount of information, by excluding the lower part of the face, including nose and mouth. Thus, both at system and cellular level, such mismatch impairs long-term functional and structural plasticity. In particular, at synaptic level, LTP induction will be favored, whereas LTD will be impaired. The black traces indicate the excitatory postsynaptic potentials in control condition; the red traces represent the long-term changes in synaptic efficacy after the induction protocol.</p>
Fig. 4 in Unique Short-Faced Miocene Seal Discovered In Grytsiv (Ukraine)
Fig. 4. Skull lateral views: A — Planopusa semenovi (NMNHU-P 64-709); B — Gulo gulo (IZYAN 896); C — Praepusa vindobonensis (IZUAN N64-468, juvenile), and D — Leptophoca lenis (CMM- V-2021).
Fig. 2 in Unique Short-Faced Miocene Seal Discovered In Grytsiv (Ukraine)
Fig. 2. Rostral portion of the partial skull of Planopusa semenovi sp. n. (NMNHU-P 64-709) in ventral (A), dorsal (B), lateral (C) and medial view (D). Upper P4 tooth in labial (E), lingual (F) and occlusal view (G). Upper M1 tooth in labial (H), lingual (I) and occlusal view (J). C — upper canine, P1 — upper first premolar; P2 — upper second premolar.
Fig. 5 in Unique Short-Faced Miocene Seal Discovered In Grytsiv (Ukraine)
Fig. 5. The single, most parsimonious Wagner tree generated by Winclada with 184 steps long, having Consistency Index of 0.75, and Retention Index 0.81.
Data from: Inversion Invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow
<p><span></span></p> <p>Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g., multiple large, old inversions that are FST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic.</p>
TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks
<p>TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.</p> <p>Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.</p>
Diet composition based on stable isotopic analysis of fecal samples revealed the preference of Black-faced Spoonbill (Platalea minor) for natural wetlands and fishponds
<p><span>Background:</span><span> Black-faced spoonbill (BFS) is a global endangered species, distributed only in the coastal zones of East Asia. Xinghua Bay is one of the main wintering sites and migration stopovers of BFS in mainland China. However, </span><span>with the </span><span>reduction and degradation of natural wetlands, it is uncertain whether the constructed wetland can provide habitat for the endangered BFS. Research on diet of BFS will help to understand their preference between natural and artificial wetlands, and also provide reference for their conservation and habitat restoration. </span></p> <p><span>Results:</span><span> In the early winter, the proportion of Palaemonidae in BFS's food was as high as 74.4%, while that of other food was only 3.0% to 6.0%. In the late winter, the food contribution of BFS was as follow: Portunidae </span><span>39.3% </span><span>> Palaemonidae </span><span>26.1% </span><span>> Cyprinidae </span><span>8.8% </span><span>></span> <span>Mugilidae </span><span>8.5% </span><span>> Gobiidae </span><span>7.3% </span><span>></span> <span>Crucian </span><span>5.1% </span><span>> Whiteshrimp </span><span>4.8%</span><span>. The proportion of Portunidae exceeded that of Palaemonidae, and together with Palaemonidae, it has become the main food of BFS in late winter. </span></p> <p><span>Conclusion: </span><span>The diet composition of BFS between the early and late winter was significantly different, which may be due to seasonal changes in food resources. Natural wetlands are the main feeding grounds of BFS, but artificial wetlands also provide them with supplementary feeding grounds and resting places. Aquaculture ponds play an important ecological function in maintaining the overwintering population of BFS in Xinghua Bay.</span></p>
Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow
<p>Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.</p> <p>We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (<em>Spizella breweri</em>) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.</p> <p>We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.</p> <p>If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.</p> <p>Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.</p>
Text-fig. 8. Progyrolepis heyleri POPLIN, 1999. a: maxilla and lower jaw of juvenile specimen in lateral view, GMC 43, whitened, scale bar 5 mm; b: maxilla and lower jaw of juvenile specimen, imprint of the maxillary medial face with a distinctive horizontal lamina, lower jaw in lateral view, GMC 83, whitened, scale bar 5 mm; c: haemal arch of the axial skeleton and fragment of a strong undivided lepidotrichium, GMC 25, whitened, scale bar 5 mm; d: drawing of the haemal arch, GMC 25, scale bar 5 mm; e: jugal in medial view with the infraorbital sensory canal and imprint of the sculpture on the lateral face of the bone, GMC 7, whitened, scale bar 5 mm. Abbreviations: ha – haemal arch, hl – horizontal lamina, hs – haemal spine, ioc – infraorbital sensory canal, lep – lepidotrichium. in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 8. Progyrolepis heyleri POPLIN, 1999. a: maxilla and lower jaw of juvenile specimen in lateral view, GMC 43, whitened, scale bar 5 mm; b: maxilla and lower jaw of juvenile specimen, imprint of the maxillary medial face with a distinctive horizontal lamina, lower jaw in lateral view, GMC 83, whitened, scale bar 5 mm; c: haemal arch of the axial skeleton and fragment of a strong undivided lepidotrichium, GMC 25, whitened, scale bar 5 mm; d: drawing of the haemal arch, GMC 25, scale bar 5 mm; e: jugal in medial view with the infraorbital sensory canal and imprint of the sculpture on the lateral face of the bone, GMC 7, whitened, scale bar 5 mm. Abbreviations: ha – haemal arch, hl – horizontal lamina, hs – haemal spine, ioc – infraorbital sensory canal, lep – lepidotrichium.
Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m).
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.