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853 results for “sign”

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zenodo48/100

Dataset: Does vendor breeding colony influence sign- and goal-tracking in Pavlovian conditioned approach?

Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did.  Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did.  Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.

opencc-by-4.0Jul 2022View details →

zenodo48/100

3D-data Runstenar signerade av Åsmund Kåresson / Runestones signed by Asmund Karasun

3D-scans of runestones signed by Asmund Karasun (Åsmund Kåresson). This dataset includes 3D-models of 11th century runestones 3D-scanned for a study within the research project Runristandets dynamik (2009-2014). The project focussed on analysis of the runic inscriptions and ornament, therefor only the inscription surfaces have been scanned. For some stones, this is the only option as they are leaning against, or inserted into, church walls. Results of analysis have been published in the article "Åsmund Kåresson - en sällskaplig runristare" (English summary) in the journal Situne Dei (Situne Dei 2016, p. 26-39; see related publications).

opencc-by-4.0Aug 2024View details →

zenodo44/100

NYU FloodSense street sign mounted distance sensor

Ultrasonic distance data in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation. Data is collected at ~5min intervals. Time fields are in local time (New York). Two types of erroneous data has been observed: <ul> <li>Large spikes in distance that always manifest at 5000mm - can be excluded</li> <li>There are ~1% rises in distance measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> This data is prelimary and is for prototyping purposes. Not to be used as a reliable data source as it is. This dataset will be updated when more data is collected. Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a>

opencc-by-4.0Oct 2020View details →

zenodo44/100

NYU FloodSense street sign mounted flood depth sensor

Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth. The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation. Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00 Two flood events have been observed in this dataset between these date ranges: <ol> <li> "2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00" </li> <li> "2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00" </li> </ol> Erroneous data has been observed: <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> This data is preliminary and is for prototyping purposes.  This dataset will be updated when more data is collected. Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a>

opencc-by-4.0Dec 2020View details →

zenodo44/100

Convex inference for community discovery in signed networks (European Parliament Voting Dataset)

This repository contains the necessary tools to reproduce the experiments of the paper <ul> <li>G. Santatmaría, V. Gómez (2015) Convex inference for community discovery in signed networks. NIPS 2015 Workshop: Networks in the Social and Information Sciences</li> </ul> The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here) The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/ <ul> <li>data/ : json files with the european votes</li> <li>network.net : signed network built from the votes</li> <li>political_parties.txt : "ground truth" party</li> <li>community_results/ : results for different number of communities and initial vertices</li> <li>dataComputations.py : used to build the signed network</li> <li>dataProcessing.py : used to build the signed network</li> </ul> We would appreciate if you cite the paper after using the data or the code. DEPENDENCIES The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04 - For PSL library, you need to have java 1.8 you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER' maven 3.x - For libDAI you will need: make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev CODE TO RUN THE FOLLOWING EXPERIMENTS: Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities INSTALL To install the experiments you have to follow the next steps: 1 Build the libdai library by doing: make -B on the folder (libdai) 2 Generate the class path of the groovy project: mvn clean install mvn dependency:build-classpath-Dmdep.outputFile=classpath.out on the psl root folder (You need to have java 1.8 and maven 3.x installed) 3 Grant exec permissions to the run.sh script Options The main python file to run the experiments is evaluatebalanceon_sn.py. It accepts the following parameters: 1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less 2 (Int) The number of underlying communities 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default. An example of execution would be: python evaluate_balance_on_sn.py 120 3 0.45 True True The results of the experiments are save in the folder results/ Scripts The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy Authors: Guillermo Santamaria & Vicenc Gomez Mar 5, 2017 For further questions, please contact vicen.gomez@upf.edu

opencc-by-4.0Dec 2014View details →

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Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"

This repository contains all raw data that were used to draw conclusions and generate figures for the paper: "Many-body quantum sign structures as non-glassy Ising models" by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A. Abstract: The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.

opencc-by-4.0Aug 2023View details →

zenodo44/100

Greek Text to Trajectories Sign Language Dataset

Entails the 2D human pose trajectories of Greek Elementary Sign Language Dataset and Greek News Sign Language Dataset (31681 examples).

opencc-by-4.0Dec 2021View details →

zenodo44/100

FloodSense street sign mounted flood depth sensor

Flood Depth Data (FDD) collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique deployment_id.  The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location.  The final depth data field is depth_proc_mm, and the raw data is dist_mm.  The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using night_median_dist_mm which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime. After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named depth_filt_mm. Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled depth_proc_mm.  This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City.  Here is the live dashboard for these sensors deployed: <a href="https://dataviz.floodnet.nyc/">FloodNet Data Dashboard</a> More about this project at <a href="https://www.floodnet.nyc/">FloodNet.NYC</a> This is an open-source project and for more information on the sensors and build manuals see the <a href="https://github.com/floodnet-nyc/flood-sensor">FloodNet FloodSensor GitHub page</a>

opencc-by-4.0Oct 2022View details →

zenodo44/100

Hypertension - Florida Annotated Corpus for Translational Science (FACTS), Vital Sign Ontology Annotations

Florida Annotated Corpus for Translational Science (FACTS), which currently consists of 20 case reports about hypertension annotated with Vital Sign Ontology (VSO) classes (version 2012-04-25).

opencc-by-4.0Feb 2018View details →

zenodo44/100

Extended Malaysian Traffic Sign Dataset (EMTD)

An extension of the existing Malaysian Traffic Sign Dataset for traffic sign (TS) detection. This contains 66 TS categories, and contains an additional 814 new TS instances than the original dataset. Containing 1,413 images in total. In particular, there has been a great increase in classes that previously had fewer than 75 examples

opencc-by-4.0Apr 2018View details →

zenodo44/100

SWL-LSE: SignaMed Word-Level LSE, a Dataset of Spanish Sign Language Health Signs

<h2>SWL-LSE Dataset</h2> The SWL-LSE dataset is coined from SignaMed Word-Level LSE (Lengua de Signos Española -Spanish Sign Language). <h2>Overview</h2> The dataset consists of 8,000 sign sequences from 300 different sign classes related to the health domain. Each class is represented by an RGB video that serves as the dictionary sign. These dictionary signs were reproduced by 124 signers, including deaf individuals, interpreters, and L2 Spanish Sign Language (LSE) students, using their webcams or mobile phones via the SignaMed platform (<a href="https://signamed.web.app" target="_new" rel="noopener">https://signamed.web.app</a>). For privacy reasons, only the skeleton data is shared. The process of collecting the dataset is described in: Vázquez-Enríquez, M.; Alba-Castro, J.L.; Pérez-Pérez, A.; Cabeza-Pereiro, C.; Docío-Fernández, L. SignaMed: a Cooperative Bilingual LSE-Spanish Dictionary in the Healthcare Domain. In Proceedings of the Proceedings of the LREC-COLING 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources; Efthimiou, E.; Fotinea, S.E.; Hanke, T.; Hochgesang, J.A.; Mesch, J.; Schulder, M., Eds., Torino, Italia, 2024; pp. 386–394.  The dataset itself and the pipeline for training and executing a baseline model based on skeletons is described in this github (https://github.com/mvazquezgts/SWL-LSE), and this paper: Vázquez-Enríquez, M.; Alba-Castro, J.L.; Docío-Fernández, L.; Rodríguez-Banga, E. SWL-LSE: A Dataset of Spanish Sign Language Health Signs with an ISLR Baseline Method. Technologies 2024, 12(10), 205, D.O.I:10.3390/technologies12100205 <h2>Files</h2> <h3>1. VIDEOS_REF.zip</h3> <ul> <li>Description: RGB videos recorded in lab conditions that represent each sign-class</li> <li>Total files: 300</li> </ul> <h3>2. videos_ref_annotations.csv</h3> <ul> <li>Description: CSV file with the correspondence between the name of the video, its class ID and gloss in spanish: FILENAME,CLASS_ID,LABEL.</li> <li>Total files: 1</li> </ul> <h3>3. ANNOTATIONS.zip</h3> <ul> <li>Description: 3 CSV files with train, validation and test file-class correspondences: FILENAME,CLASS_ID</li> <li>Total files: 3</li> </ul> <h3>4. MEDIAPIPE.zip</h3> <ul> <li>Description: Pickle files containing the full output of Mediapipe using their Heavy model. Each .pkl file contains the outputs of Mediapipe Holistic legacy, Mediapipe Pose and Mediapipe Hands. Each file is package as a dictionary: dict_keys(['pose', 'hands', 'holistic_legacy'])</li> <li>Total files: 8000</li> </ul> <h2>Usage</h2> Researchers and practitioners in pattern recognition, machine learning, and sign language linguistics may find this dataset valuable for: <ul> <li>Training/testing machine learning models for isolated sign language recognition or gesture recognition.</li> <li>Analyzing patterns on signs realization</li> </ul> <h2>Acknowledgments</h2> This dataset is a collaborative effort of the next research goups and entities: <ul> <li><a href="http://gtm.uvigo.es/en/">Group of Multimedia Technologies (GTM)</a> from the <a href="https://atlanttic.uvigo.es/en">atlanTTic Research Center</a> of <a href="http://www.uvigo.es/">University of Vigo</a> (Spain)</li> <li><a href="http://grades.uvigo.gal/">Group of Discourse and Society (GRADES)</a> from the <a href="https://fft.uvigo.es/en/">School of Philology and Translation</a> of <a href="http://www.uvigo.es/">University of Vigo</a> (Spain)</li> <li><a href="http://www.faxpg.es/">Federation of Deaf People Galician Associations (FAXPG)</a></li> <li><a href="https://fundacioncnse-dilse.org">Fundación CNSE-DILSE</a></li> </ul> Gratitude is extended to them for their contributions and support.

opencc-by-4.0Sep 2024View details →

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SARS-CoV-2 Infection and Clinical Signs in Cats and Dogs from Confirmed Positive Households in Germany

Supplemental material and raw data referring to specified publication

opencc-by-4.0Mar 2023View details →

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Neural Joint Space Implicit Signed Distance Functions [Data & Code]

These data files containg code sources for dataset creation & model learning (neural-jsdf.zip) and collected synthetic dataset of free & collided postures for robotic arm Franka (sdf_3m_full_mesh.mat). Follow the Readme.MD files to launch the code if needed. Corresponding Git repo: https://github.com/epfl-lasa/Neural-JSDF

opencc-by-4.0Dec 2022View details →

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Code and Data for: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"

Code and data for manuscript: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"

opencc-by-4.0Nov 2020View details →

zenodo40/100

Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.   We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).   Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots. In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. Hot and cold spots for each sign are publicly available as binary labels in NIfTI format.

opencc-by-4.0Jan 2021View details →

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NYU FloodSense street sign mounted flood depth sensor

opencc-by-4.0Dec 2020View details →

zenodo40/100

Charles S. Peirce's model of the sign

Charles S. Peirce's model of the sign based in his various writings. The image does not appear in any of them, but is based on Peirce's theory.

opencc-by-4.0Mar 2017View details →

zenodo40/100

Linguistic sign according to Ronald Langacker

The model of the tinguistic sign according to Ronald W. Langacker as described in Foundations of Cognitive Grammar (1987). The image does not appear in the book, but is based on it.

opencc-by-4.0Mar 2017View details →

zenodo40/100

Ferdinand de Saussure's model of the sign

Ferdinand de Saussure's model of the sign as described in Course in General Linguistics (1916). The image does not appear in the book, but is based on it.

opencc-by-4.0Mar 2017View details →

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Fig. 1 in Diversity of intestinal protozoa and clinical signs associated in wild-caught Phoneutria nigriventer kept in captivity for the anti-arachnid serum production

Fig. 1. Phoneutria nigriventer kept in glass containers with a humidified cotton ball and a cardboard substrate.

opencc-by-4.0Apr 2022View details →

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Allen Brain Atlas

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allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record