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5,481 results for “people”
Tikar People Male Medicine Totem Figure
From the Tikar People of the Republic of Congo. One of a kind, hand-made terra cotta with dark brown vegetable patina accented with red camwood powder and white kaolin in the form of a male figure standing with a wrestler-like pose & body, smiling face with overall facial scarification, round rings around neck, wrist, waist & ankle, leaf patterned scarification on torso & forehead, arms arched backward with hands rounded & held like a cup, strong powerful legs, small leather packet with herbal medicines with shells on either side secured above groin, shell necklace, back has round opening at base of neck between shoulder blades for insertion of herbal medicines. Source: Objaverse 1.0 / Sketchfab
Yorùbá People Wooden Mask (Gelede)
From the Yorùbá People of Nigeria. A "Gelede" mask honors the power of the female forces in the cosmos, especially elderly women. The mask is worn during dancing, singing and drumming. The festival atmosphere encourages women to use their power to promote the well-being of their community. Source: Objaverse 1.0 / Sketchfab
Baule People Mouse Divination Pot
Diviners (highly respected fortue tellers) use a mouse and a divination pot called a "gbekre" to predict future events. Source: Objaverse 1.0 / Sketchfab
Senufo People Diviner's Horse & Rider Figure
From the Senufo People of Cote D'Ivoire. Carved wood with varied light & dark brown patina of simple male figure with serene expression, wearing cap & amulet around neck sitting astride an abstract horse figure with two block-like legs, saddle, holding reins in his right hand. Source: Objaverse 1.0 / Sketchfab
Baule People Ceremonial Fly Whisk Handle
From the Baule people of Cote d'Ivoire. This fly whisk has a rounded, circular handle with an elephant standing on top of it. The handle is carved wood with a brown patina, covered with a 24k red gold leaf. The shaft of the handle has ancestral faces. The horse tail attachment for shooting flies is missing. Source: Objaverse 1.0 / Sketchfab
Dan Yakuba People Poro Society Fire Runner Mask
Carved wood with dark brown patina of face in oval form with minimal features, round eyes overlaid with white metal and red fabric covering entire eye area, flat nose, large lips with small opening, indigenous blue & white fabric with 3 rows of cowrie shells adorns crown, indigenous fabric with handmade bells, cowrie shells and herbal medicine packets affixed to chin, holes around outer rim for attachment. Source: Objaverse 1.0 / Sketchfab
Punu People Ceremonial Dance Mask
From the Punu People of Equitorial Guinea. Carved wood with fine aged encrusted dark brown black vegetable pigment& white kaolin patina of a mask in the form of a human face, probably female, covered primarily in kaolin with forehead, temple & cheek scarification, eyelid area, ears & lips accented with dark brown black vegetable pigment coloration, unusual pair breast-like forms on top of mask as well as unusual scalloped design around the perimeter of the mask which is also pierced for attachment, signs of age and use. Source: Objaverse 1.0 / Sketchfab
Kõtkõra asisi 'Kõtkõra's corn' or 'The origin of maize and other crops among the Sakurabiat people'
<p>This short narrative is a fragment of a mythological tale that describes the origin<br> of maize and other crops, such as beans and manioc (yucca), among the Sakurabiat people. Sakurabiat is pronounced [sa’kɨrabiat]. In the orthographic convention for the language the grapheme <u> represents the hight central vowel<br> [ɨ].The Sakurabiat are very reduced in number. In the last survey done in 2016,<br> there were only 65 people living in the Rio Mekens Indigenous Land.<br> The Kõtkõra asisi story is told by Mercedes Guaratira Sakyrabiar, one of the<br> oldest speakers of Sakurabiat at the time of the recording. Sadly, she passed away<br> in December of 2015. Mercedes’s age was not known for certain, but she was<br> believed to be more than 75 years old when she told this story in 2006. The story<br> was recorded in audio as part of a long term project for the documentation and<br> study of the Sakurabiat (Mekens) language, which had partial support from the<br> Endangered Language Documentation Program, funded by the School of Oriental<br> and African Studies (SOAS).</p> <p> </p> <p> </p> <p>This dataset contains the mediafiles. A glossed version with annotations is found in<br> On this and other worlds -- Voices from Amazonia<br> Edited by Kristine Stenzel and Bruna Franchetto</p>
Wildlife following people: A multidisciplinary assessment of the ancient colonization of the Mediterranean Basin by a long-lived raptor
<p>Here, we used a multidisciplinary approach to a) reconstruct the process of colonization of the Mediterranean Basin by a long-lived bird of prey, the Bonelli's eagle (<em>Aquila fasciata</em>), and b) test the hypothesis that this colonization was unintentionally favored by anatomically modern humans through a release of competition by dominant species, primarily golden eagles (<em>A. chrysaetos</em>). For this, we used distribution, genetics, fossils, and ecological data. Data archived in Dryad includes all genetic information (<strong>Genetic_data.xlsx</strong>) and a portion of the ecological data (<strong>Ecological_data-provinces.xlsx</strong>, <strong>Ecological_data-nuclei.xlsx</strong>, <strong>Ecological_data-GLMs.xlsx</strong>, <strong>Ecological_data-reciprocal_occupation.xlsx</strong>), while the remaining data can be accessed from a) tables included in the manuscript and b) the original sources.</p>
Spotlight Project Data - How people of colour experience and engage with climate change in Britain
<div> <div> <div> <div> <p>This repository contains data from a survey which was designed by Charles Ogunbode and Jeremy Kidwell to capture climate change-related experiences, perceptions, emotions, actions and policy preferences among UK people of colour. We are grateful for input on instrument design and analysis by Nick Anim, Dr Amiera Sawas and Serayna Solanki as well as from the project steering group: Zarina Ahmad (University of Manchester), Sara Jane Nii-Adjei (Christian Aid), Meena Rajput (Greenpeace), Reuben Fakoya-Brooks (REED Ecological Network/British Ecological Society) and Dr Neema Begum (University of Nottingham).</p> <p>Respondents included ethnic minority UK residents aged 18 years or over (N = 1,008) and the sample was gathered in March 2022 by Qualtrics Research Services. The instrument was pre-tested with a smaller number of respondents (N = 180) on 25 Feb 2022.</p> <p>The data is provided in plain-text tab-separated values format (.tsv) as well as in SPSS .sav file format. The latter is recommended for analysis, and there is an associated code repository (linked below) which contains R code used to load and analyse this data (using the haven() SPSS library). We have also included the original instrument used to collect survey data in qualtrics .qsf format and docx format. Please note: some data columns have been redacted and are not included in this data release.</p> <p>The research was funded by a Research England QR-PSF award made through the University of Birmingham.</p> <p>We very much hope that by making this data as widely available as possible, other colleagues may find opportunities to expand on this research. Towards that end, we have also made available (a) <a href="https://zenodo.org/records/11130504">an open-access plain language report </a>analysing this data, (b) <a href="https://github.com/climate-experiences/spotlight-report">all of the R code written to produce the analysis in our report</a> which others may freely use and appropriate and (c) an open access online textbook which introduces the R programming language to researchers who may not have existing data science expereince. <a href="https://kidwellj.github.io/hacking_religion_textbook/chapter_2.html">Chapter two of that textbook</a> makes explicit (and more basic) use of this dataset with specific examples and explanations of how to build the code.</p> </div> </div> </div> </div>
Characterizing the Interaction of the Effects of a Museum Visit on Mobility, Cognition, and Well-being in People with Stroke
<h2><strong>Study description</strong></h2> <p>This exploratory small case study operated by Medical Physics Laboratory & Digital Innovation of School of Medicine of Aristotle University of Thessaloniki (AUTH) is a collaborative study between AUTH and McGill-CRIR-Canada, and for this reason it followed and replicated part of the protocol of the Canadian VITALISE partners described above. The aim of this particular small-case study was to investigate the interaction of the effects of a museum visit (Museum of Casts and Antiquities in Aristotle University of Thessaloniki) on mobility, cognition, well-being and the experience of individuals, as well as to investigate whether cognitively and verbally accessible audio guides of exhibits contribute to a better understanding of the descriptions of these exhibits, through measurements made using advanced technologies. In this context, the data collected aimed also to draw some conclusions regarding the feasibility and user satisfaction of conducting a range of different measurements and interventions in a museum setting.</p> <p>Piloting phase: The piloting phase began in May 2023 and was concluded at the end of the year. Prior to the museum visit, the participants were administered some questionnaires in the Thessaloniki Action for HeAlth & Wellbeing Living Lab - Thess-AHALL, aiming to obtain a holistic view on their cognitive status, as well as dimensions of their physical and mental wellbeing and quality of life. <a href="../api/records/11473970/draft/files/Questionnaires%20during%20the%20museum%20visit.xlsx/content" target="_blank" rel="noopener noreferrer">Questionnaires during the museum visit.xlsx</a></p> <p>Afterwards, the participants, one at a time, entered the museum with the members from the project’s research team, where they were asked to wear some specific equipment. In particular, they initially wore a pair of smart insoles (Digitsole Pro Smart Insoles) (<a href="../api/records/11473970/draft/files/Digitsole-pro-museum-dataset.csv/content" target="_blank" rel="noopener noreferrer">Digitsole-pro-museum-dataset.csv</a>) and a smart watch (Smartwatch-Fitbit Charge 5) <a href="../api/records/11473970/draft/files/Fitbit%20Datasets.zip/content" target="_blank" rel="noopener">Fitbit Datasets.zip</a> and were asked to walk at their own pace following a predetermined guided tour route through the museum spaces and exhibits. Breaks were provided whenever requested by the participants.<br>After completing their guided tour in the museum, they were asked to wear eye-tracking glasses (Pupil Labs) <a href="../api/records/11473970/draft/files/Pupils-dataset.zip/content" target="_blank" rel="noopener noreferrer">Pupils-dataset.zip</a> and headphones to listen to audio descriptions (audio guides) of three selected exhibits, the Tombstone, the Hermes of Praxiteles and the West Pediment from the temple of Zeus at Olympia. Specifically, they were asked to stand in front of three (3) exhibits and listen to the description. For each of the three exhibits, participants first listened to their original description (a description that someone can usually find next to the exhibit or in a relevant educational textbook) and then they listened to the modified, accessible version of the exhibit description (with simpler words, shorter sentences etc.). After each audio description (original and modified), they were asked to answer a number of predetermined questions related to the audio description they had just heard.</p> <p>At the end, after the aforementioned equipment was removed, they were asked to answer a number of questions and questionnaires about their overall museum experience and potential challenges or stress experienced (Satisfaction and feasibility questionnaire, Visual Analog Stress scale, State-Trait Anxiety Inventory) <a href="../api/records/11473970/draft/files/Analysis%20feasibility%20&%20satisfaction%20questionnaire.xlsx/content" target="_blank" rel="noopener noreferrer">Analysis feasibility & satisfaction questionnaire.xlsx</a>.</p>
Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: the case of Prague
<p>This is supplementary data for a paper called ‘Where is the heat threat in a city? Different perspectives of people-oriented and remote sensing methods, the case of Prague’ to be submitted to the journal Heliyon. Dataset contains three folders:</p> <ol> <li><strong>LST</strong> <br>– Layer Landsat_ecostress_data = vector polygon layer containing fishnet which includes values from 15 sattelite images from 3 different sources - ECOSTRESS, Landsat 8 and Landsat 9<br>Attribute percent_av contains mean percentile from all 15 images</li> <li><strong>Participatory mapping</strong><br>Raw data from participatory mapping campaign held on August 2022 in Prague-Holešovice</li> <li><strong>Thermal walk</strong><br>Layer Data_app = Raw data from thermal walk held on August 3rd 2022 (Declared time in the attribute table is UTC)<br>Layer DataApp_corr = Corrected coordinates from thermal walk (For these corrections, precisely prepared routes were used. Data were post-processed using an algorithm for a minimization of the distance between the measured and expected point location. The measured point was moved using its normal on-route position and his distance was compared with the previous point. If the distance was larger than 115% of expected, the point was moved closer, and when the distance was smaller than 85% of expected, the point was moved forward.)</li> </ol> <p> </p> <p><em>Acknowledgements: This work was supported by the Faculty of Science, Palacký University Olomouc internal grant IGA_PrF_2024_022 – Novel approaches to studying the human thermal environment in urban areas. This work was also supported by Strategy AV21 project ‘City as Lab of changes’, financed by the Czech Academy of Sciences.</em></p>
An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
<p>This repository is related with the next paper. Please cite us if this code is useful to you.</p> <p>Barral, V., Dominguez-Bolano, T., Escudero, C. J., & Garcia-Naya, J. A. An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting.</p> <h2>Python Scripts</h2> <ul> <li><em>count_plot.py</em>: Generates a plot comparing image tracking estimation, radar with DBSCAN, and radar with OPTICS. Use example:</li> </ul> <pre><code>python count_plot.py count_video_full.log count_radar_moving_average_30_optics.log count_radar_moving_average_30_dbscan.log "Video" "Radar with OPTICS" "Radar with DBSCAN" average_count_full_optics_dbscan.pdf </code></pre> <h2>Radar measurements</h2> <ul> <li><em>test_0.bag</em>: FMCW mmWave radar measurements from three IWR6843 devices (ISK and AOP). Is a ROS (Robotic Operative System) log, can be played with <code>rosbag play test_0.bag</code></li> </ul> <h3>Radar people counting logs</h3> <ul> <li> <p><strong>count_radar_moving_average_30_dbscan.log</strong>: radar people counting estimation using a 30 seconds moving average and DBSCAN as clustering algorithm.</p> </li> <li> <p><strong>count_radar_moving_average_30_optics.log</strong>: radar people counting estimation using a 30 seconds moving average and OPTICS as clustering algorithm.</p> </li> </ul> <h2>Image tracking</h2> <h3>Videos</h3> <ul> <li><em>test_0_cam_0_anonymized.mp4</em>: Camera 0 capture.</li> <li><em>test_0_cam_1_anonymized.mp4</em>: Camera 1 capture.</li> </ul> <h3>Image tracking logs</h3> <ul> <li><em>test_0_cam_0_interp.txt</em>: People count in camera 0</li> <li><em>test_0_cam_1_interp.txt</em>: People count in camera 1</li> </ul> <p>The files have the following format:</p> <p>frame_number, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z The coordinates x, y, z are always -1 The bb fields define the bounding box of the detection If multiple people are detected in a frame, there are several lines with that frame_number, but with different track_ids.</p> <h3>Image people counting logs</h3> <ul> <li><strong>count_video_full.log</strong>: image tracking people counting estimation using a 30 seconds moving average.</li> </ul>
Emotions of Social Eating for People with Selective Eating Habits (ESE1)
<p><strong><em>Emotions of Social Eating for </em></strong><strong><em>People with Selective Eating Habits (ESE1) data set</em></strong></p> <p> </p>
UNICITY: A depth maps database for people detection in security airlocks
<p><strong>UNICITY: A depth maps database for people detection in security airlocks.</strong></p> <p>UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries, like for instance tailgating (when a person walks very close to another to get into a restricted area). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</p> <p><strong>Main Features:</strong></p> <ul> <li>UNICITY consists of 58k images using two depth sensors.</li> <li>65 recorded sequences with one or two people performing different behaviors such as attacks and tailgating.</li> <li>UNICITY also provides code for evaluation and visualization, and full annotation of people such as the location of head and shoulders.</li> <li>This new dataset is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</li> </ul> <p><strong>Citation</strong>:</p> <p>Please cite the following paper if you use the UNICITY dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>UNICITY: A depth maps database for people detection in security airlocks. J. Dumoulin, O. Canevet, M. Villamizar, H. Nunes, O.A. Khaled, E. Mugellini, F. Moscheni, and J.M Odobez. International Conference on Advanced Video and Signal-based Surveillance Workshop (AVSSW). November 2018.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>Joël Dumoulin, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Olivier Canévet, Idiap Research Institute, Martigny, Switzerland.</li> <li>Michael Villamizar, Idiap Research Institute, Martigny, Switzerland.</li> <li>Hugo Nunes, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Omar Abou Khaled, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Elena Mugellini, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Fabrice Moscheni, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Jean-Marc Odobez, Idiap Research Institute, Martigny, Switzerland.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The work was supported by Innosuisse, the Swiss innovation agency, through the UNICITY (3D scene understanding through machine learning to secure entrance zones) project.</p> <p><strong>Links:</strong></p> <p>Next links contain additional information about the dataset:</p> <ul> <li>Innosuisse UNICITY project: <a href="https://www.idiap.ch/en/scientific-research/projects/UNICITY">[link]</a></li> <li>Paper describing the dataset: <a href="http://publications.idiap.ch/index.php/publications/show/3939">[link]</a></li> <li>Video presenting the dataset: <a href="https://www.youtube.com/watch?time_continue=2&v=pGrnI12OhmA">[link]</a></li> <li>Paper using the dataset for counting people and detecting intrusions: <a href="http://michael-villamizar.com/avss18.html">[link]</a> <ul> <li>WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.<br> M. Villamizar, A. Martinez-Gonzalez, O. Canevet and J-M. Odobez.<br> International Conference on Advanced Video and Signal-based Surveillance (AVSS) - 2018.</li> </ul> </li> </ul> <p><strong>Contact</strong>:</p> <p>For any questions, please contact:</p> <ul> <li>Michael Villamizar, Idiap Research Institute, Martigny -Switzerland</li> </ul>
Crunchbase in RDF: A Large Data Set About Jobs, Websites, Organizations, News, People, Products, and Acquisitions
<p><strong>CrunchBase</strong> in an online platform providing information about startups and technology companies, including related entities such as the products they sell, key people they employ, and investments they made and received.</p> <p>We provide here an <strong>RDF data set of Crunchbase</strong> as of October 2015. The data set contains information about</p> <ul> <li>1,946,435 jobs</li> <li>1,348,449 websites</li> <li>567,937 organizations</li> <li>519,763 news</li> <li>430,093 people</li> <li>60,076 products, and</li> <li>33,127 acquisitions.</li> </ul> <p>The data set has been used, among other things, for data integration with financial data sources to evaluate the performance of particular companies and for monitoring news to find statements that are not in Crunchbase as an RDF knowledge graph yet.</p> <p>Note that the provided data set was created in October 2015 when all Crunchbase data was <strong>licensed under Creative Commons Attribution-NonCommercial License 4.0 (CC-BY-NC) and partly under Creative Commons Attribution License 4.0 (CC-BY)</strong>. Also the provied<strong> data set is licensed under these licenses.</strong> Concerning licensing of current Crunchbase data, we can refer to <a href="https://about.crunchbase.com/terms-of-service/">https://about.crunchbase.com/terms-of-service/</a>.</p> <p>For <strong>more information</strong> about the data set, see our paper <a href="http://dbis.informatik.uni-freiburg.de/content/team/faerber/papers/CrunchBaseWrapper_SWJ2017.pdf">A Linked Data Wrapper for CrunchBase.</a></p> <p>When you use the data set, please <strong>cite</strong> us as follows:</p> <blockquote> <p>Michael Färber, Carsten Menne, Andreas Harth. “A Linked Data Wrapper for CrunchBase”. In: Semantic Web Journal 9(4). IOS Press, 2018, pp. 505–5015. (<a href="https://dblp.org/rec/bibtex/journals/semweb/FarberMH18">BibTeX entry at DBLP</a>)</p> </blockquote>
Dataset: User side acquisition of People-Centric Sensing in the Internet-of-Things
<p>- This archive contains the files submitted to the 2nd International<br> Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files<br> provided in this package are associated with the paper titled<br> "Dataset: User side acquisition of People-Centric Sensing in the<br> Internet-of-Things"</p> <p>- Content of the package:</p> <p> + 1_beacon_table.pkl: The beacon table in Pickle format. It contains<br> 20612286 data points where each data point represents a Bluetooth<br> beacon with 15 attributes as follows: <_id, host_id, ble_address,<br> sound_avg_peak, sound_max_peak, sound_count_over_thres_per_frame,<br> sound_avg_all, sound_avg_over_thres, temperature, humidity,<br> pressure, eco2_ppm, tvoc_ppb, rssi, timestamp>.</p> <p> + 2_device_description_table.pkl: The device description table<br> provides the mapping between a device's Bluetooth address and its<br> physical identity (device_id, description, type).</p> <p> + 3_checkin_table.pkl: The check-in table provides a timeseries of<br> user interactions with three Android tablets (i.e. tuples of <time,<br> host_id, checkpoint device>).</p> <p> + 4_sample_beacon_table.pkl: The sample beacon table in Pickle<br> format. It contains 1000 data points where each data point<br> represents a Bluetooth beacon with 15 attributes as follows: <_id,<br> host_id, ble_address, sound_avg_peak, sound_max_peak,<br> sound_count_over_thres_per_frame, sound_avg_all,<br> sound_avg_over_thres, temperature, humidity, pressure, eco2_ppm,<br> tvoc_ppb, rssi, timestamp>.</p> <p> + 5_sample_device_description_table.pkl: The sample device description<br> table provides the mapping between a device's Bluetooth address and<br> its physical identity (device_id, description, type).</p> <p> + 6_sample_checkin_table.pkl: The check-in table provides a<br> timeseries of user interactions with three Android tablets<br> (i.e. tuples of <time, host_id, checkpoint device>).</p> <p> + print_table_heads.py: A Python script which fetches Pickle tables<br> as DataFrames and prints out the sample entries.</p> <p><br> - ACM Reference Format: Chenguang Liu, Jie Hua, Tomasz Kalbarczyk,<br> Sangsu Lee, and Christine Julien. 2019. Dataset: User side<br> acquisition of People-Centric Sensing in the Internet-of-Things. In<br> The 2nd Workshop on Data Acquisition To Analysis(DATA’19), November<br> 10, 2019, New York, NY, USA. ACM, New York, NY, USA, 3 pages.<br> https://doi.org/10.1145/3359427.3361914</p>
Supplementary file 23 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 22 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 21 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</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.