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44 results for “virtual collection”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
FIG. 12 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 12. — Workflow of the Annotate-On tool. A project allows the study, annotation and measurement of objects within images from the Récolnat database, URLs or any hard-drive to constitute a library. It uses one or more of the data models defined previously. Key words are used to select subsets within the library and the results can be exported to various external data processing tools.
FIG. 15 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 15. — Scales on Paris herbarium sheets. The vertical line on the left measures 10 mm, on the right the label of the linear barcodes measures 50 mm in length. In addition, the photos of the nomenclatural types have a scale.
FIG. 13 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 13. — Definition of a new character ("apex angle") for the model "Asparagus", here a physical measurement (angle unity: "°") into the group named "Cladode".
FIG. 9 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 9. — Annotation of a categorical character "apex shape" with the character state or nominal value "accuminate" (red arrow).
FIG. 1 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 1. — Examples of mean (M) and standard deviation (SD). Two characters: blade length (FII L) and blade width (fll- larg), number of measures (#). First line: image measures; second line: batch measures (global value).
Linked collectors and determiners for: PC herbarium - Cryptogamy Collection at the Muséum National d'Histoire Naturelle - Herbário Virtual REFLORA.
Natural history specimen data linked to collectors and determiners held within, "PC herbarium - Cryptogamy Collection at the Muséum National d'Histoire Naturelle - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a1d395f9-075e-4a41-9782-ad5dc56bcece">https://bionomia.net/dataset/a1d395f9-075e-4a41-9782-ad5dc56bcece</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a1d395f9-075e-4a41-9782-ad5dc56bcece">https://gbif.org/dataset/a1d395f9-075e-4a41-9782-ad5dc56bcece</a>. Formatted as a Frictionless Data package.
A collection of Sign Language Narations by Virtual Humans in the Greek Sign Language
<p>A collection of Sign Language Narations by Virtual Humans in the Greek Sign Language in the context of the Mastic Pilot of Mingei.</p>
eCodicesNL: a virtual library for medieval manuscripts in Dutch collections
<p>This is a presentation about eCodicesNL: a new initiative to build a virtual library for medieval manuscripts in Dutch collections. The elements that go into this library are, just as in its model project e-codices Switzerland, high quality and complete images, high quality descriptions and an excellent search interface. The concept is simple, but the execution is not: what is the best model for creating such an aggregated library, and how can we deal with data that already exist? How can we facilitate collections to take care of their own material, and keep it updated within a collective system?</p>
Timely Dictionary Development: In-person and Virtual Rapid Word Collection for Endangered Indigenous Languages
<p>Timely Dictionary Development: In-person and virtual Rapid Word Collection for endangered Indigenous Languages</p> <p>Dorothea Hoffmann, Wilhelm Meya, Abbie Hantgan-Sonko & Elliot Thornton</p> <p>Presented 5 October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities Where Do We Need to Go From Here? Language Documentation and Archiving in the International Decade of Indigenous Languages</p>
Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment
<p>Title: Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment</p> <p>Abstract:</p> <p>As VR technologies continue to evolve and gain popularity, one of their most notable features is connecting with the virtual presence of a person who is not physically present. Understanding the dynamics of user interaction within these environments is crucial, as they can be utilized in various ways, including collaboration, communication, social interactions, or games and entertainment. This paper presents a method for measuring collaboration and co-presence factors of users by developing a real-time data collection and analysis framework. The designed framework focuses on different collaboration and co-presence scenarios and evaluates a comprehensive system for monitoring and analyzing user interactions in VR, employing both physiological sensors and subjective feedback to assess the sense of presence, co-presence, and collaboration quality. Through an extensive literature review, the paper studies how various factors, including avatar realism and communication modalities, influence user engagement and interaction efficacy. The experiment framework’s capability to integrate qualitative and quantitative data provides a deeper understanding of the immersive experience and its impact on collaborative tasks. The results highlight the importance of design choices in VR environments and their implications for human-computer interaction, user performance, and satisfaction. The findings offer practical guidance for developing more effective VR systems for collaborative work and social interaction.</p> <p>Data Description:</p> <p>1. User Interaction Logs:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Timestamped logs of user actions and interactions within the VR environment, including movement data, interaction with objects, and communication instances.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Timestamp, Action Type, Object Interacted, Coordinates, Duration</p> <p>2. Physiological Sensor Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Real-time physiological data collected from users during VR sessions, including heart rate, skin conductance, and EEG data.</p> <p><span> </span>- Format: CSV,</p> <p><span> </span>- Variables: User ID, Timestamp, Heart Rate, Skin Conductance, EEG Channels</p> <p>3. Avatar Realism and Communication Modalities Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Data evaluating the impact of avatar realism and communication methods (e.g., voice chat, text chat) on user engagement and interaction efficacy.</p> <p><span> </span>- Format: CSV, Text</p> <p><span> </span>- Variables: User ID, Avatar Type, Communication Modality, Engagement Score, Interaction Quality Feedback</p> <p>4. Collaboration and Co-presence Metrics:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Calculated metrics for collaboration efficiency and co-presence, derived from interaction logs and physiological data.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Collaboration Efficiency Score, Co-presence Score, Task Performance</p> <p> </p> <p> </p>
FIG. 11. — Adding a in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 11. — Adding a cartel label on an image.
FIG. 2 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 2. — Example of measurements: linear (green line) or non-linear (red line).
FIG. 6 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 6. — Example of the use of rectangle of interest (ROI) to show features on a specimen image.
FIG. 10 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 10. — Adding text to an image.
FIG. 4 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 4. — Example of angle measurements on an orchid seed from the genus Phaius.
FIG. 3 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 3. — Example of measurements: surface and perimeters.
FIG. 8 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 8. — Transcription of a written area into a plate.
FIG. 14 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 14. — One of the three modes of importing images,drag and drop from a computer hard drive.
FIG. 5 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 5. — Example of organ counts:number of rays in an umbel.
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.