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558 results for “Training Data”
Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstruction
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Data from: Cadaveric emergency cricothyrotomy training for non-surgeons using a bronchoscopy-enhanced curriculum
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Raw data of: Spelling training "Errorless learning" using Tablet PC in elementary schools
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Decoratype-based materials informatics: Polaritype identification, convex hull DFT calculations, training data, and predicted compounds data
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Data and code to run birdnet-discovery, a pipeline for signal discovery and training dataset creation using BirdNET embeddings, including example data from acoustic ARUs in Northern Alaska
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Data from: Development of a 3D simulator for training the mouse in utero electroporation
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Testing and training data sets for: A novel representation of time-resolved particle emissions from pyrolyzing wood
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Data from: Training data from SPCAM for machine learning in moist physics
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Training data for "Differential exon usage analysis"
<p>In RNA-Seq, we usually want to know the differentially expressed genes, as explained in several Galaxy Training Material tutorials. Sometimes,the question is more "which exons are differentially expressed". The process to identify differentially expressed exons is really similar to the one for differentially expressed genes.</p> <p>In this tutorial, we identify exons regulated by the <em>Pasilla</em> gene using RNA-Seq data from <a href="https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#brooks2011conservation">Brooks <em>et al.</em> 2011</a>.</p>
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Training and Testing Data, Associated Code, and SCAM Validations Code and Data for ResNet in moist physics (ResCu)
<p>Data and codes for a deep convolutional residual neural network moist physics parameterization (ResCu).</p> <p>In this new version, the randomly selected training data samples and part of testing data samples (June, July and August) are provided. They are processed into a new data structure, which can be directly utilized in training and testing. For the entire second year training samples and the entire third year testing samples, we provide them in a repository at Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF).</p> <p>Please download and decompress ResCu_Han_et_al_JAMES.tar.gz.</p> <p>Follow the instructions in README.txt and download the training and testing data (The Dryad depositary is provided in the description). </p> <p>Here we provide 3 parts of data and codes:</p> <p>1, Training and testing data from SPCAM;</p> <p>2, Training and testing codes for ResCu and many other NN architectures;</p> <p>3, SCAM validations.</p> <p> </p>
Training Data For Random Forest Classification
<p>This is the training data for land use/cover classification and it was collected by doing visual interpretation from Google Earth Pro.</p>
Training Data
<p>This is the training data for land use/cover classification and it was collected by doing visual interpretation from Google Earth Pro.</p>
Data for Galaxy training in assembly of Lumpy Skin Disease Virus Genome
<p>This upload was to support a Galaxy tutorial on the Lumpy Skin Disease virus genome prepared by the Defend2020 project. To access the data deposited in Genbank and the Sequence Read Archive please refer to the following deposits.</p> <p>The LSDV isolate Kubash/KAZ/16 sequence has been deposited in GenBank under accession number <a href="https://www.ncbi.nlm.nih.gov/nuccore/MN642592">MN642592</a>, and raw data have been submitted to the SRA under BioProject number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA587601/">PRJNA587601</a>.</p>
Dataset for: Sensitivity of a satellite algorithm for harmful algal blooms discrimination to the use of laboratory bio-optical data for training
<p>Two files relating to the publication by Martinez-Vicente et al. (2020).</p> <p>meris_data_karenia_alt_chla.xlsx : file containing the chlorophyll concentrations for the different areas in the MODIS images selected for training and evaluation of the algorithm.</p> <p>coefficients_for_LDA_Karenia_mikimotoi.zip: file containing the coefficients for the Linear Discriminant Analysis (LDA) resulting from the training datasets 1,2 and 3. </p> <p> </p>
Data on universities offering undergraduate degrees that train students for soil science careers at universities in the USA and its territories
<p>Several soil science education studies over the last 15 years have focused on the number of students enrolled in soil science programs. However, no studies have quantitatively addressed the number of undergraduate soil science preparatory programs that exist in the United States, which means we do not have solid data concerning whether overall program numbers are declining, rising, or holding steady. This also means we do not have complete data on the same trends for total undergraduate soil science students in the United States. This study used the US Office of Personnel Management (OPM) Soil Science Series 0470 standards to determine if a bachelor's degree met soil science preparatory criteria. Lists of the approximately 3,500 regionally accredited colleges and universities were obtained from the regional accrediting agencies and the website of each of the colleges and universities was visited to determine if they had a degree program that met the OPM 0470 criteria. A total of 92 soil science preparatory degree programs were identified at 86 colleges and universities. These programs were primarily linked to 1) agriculture, 2) environmental science, and 3) soil and water science based on number of degree occurrences. This study creates a baseline for future studies that can investigate trends in soil science programs. It also provides insight into the institutions and degree programs that should be included in soil science education studies.</p>
Data from: BCI training to move a virtual hand reduces phantom limb pain: a randomized crossover trial
<p>Objective: To determine whether training with a brain–computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.</p> <p>Methods: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The two trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608).</p> <p>Results: VAS at day 4 was significantly reduced from the baseline after real training (45.3 [24.2] to 30.9 [20.6], 1/100mm, mean [SDs]; P=0.009<0.025), but not after random training (P=0.047>0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (P<0.01).</p> <p>Conclusion: Three-day training to move the hand images controlled by BCI significantly reduced pain for one week.<br> Classification of evidence: This study provides Class &#8546; evidence that BCI reduces phantom limb pain.</p> <p> </p>
Data from: Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes – a pilot randomized controlled trial
Objectives: The objective was to investigate the feasibility and usability of structured text-messages, goal-setting and phone-calls on adherence to a 12-week self-conducted interval walking training (IWT) program, delivered by the InterWalk smartphone among patients with type 2 diabetes (T2D). Methods: In a two-arm pilot randomized controlled trial (Denmark, March 2014 to February 2015), patients with T2D (18-80 years with a Body Mass Index of 18 and 40 kg/m2) were randomly allocated to 12 weeks of IWT with (intervention) or without additional support (control). The primary outcome was the difference between groups in accumulated time of interval walking training across 12 weeks. All patients were encouraged to use the InterWalk application to perform IWT for ≥90 minute/week. Patients in the intervention group made individual goals regarding lifestyle change, received automated text-messages once a week, inquiring about exercise adherence. In case of consistent non-adherence, the patients would receive a phone-call inquiring about the reason for non-adherence. The control group did not receive additional support. Information about training adherence was assessed objectively. Usability of structured text-messages was assessed based on response rates and self-reported satisfaction after 12-weeks. Results: Thirty-seven patients with T2D (66 years, 65% female, hemoglobin 1Ac 50.3 mmol/mol) where included (n=18 and n=19 in intervention and control group, respectively). The retention rate was 83%. The intervention group accumulated [95%CI] 345 -7, 698 minutes of IWT more than the control group. The response rate for the text-messages was 83% (68% for males and 90% for females). Forty-one percent of the intervention and 25% of the control group were very satisfied with their participation. Conclusion: The combination of structured text-messages, goal-setting with the possibility of follow-up phone calls are considered feasible interventions to attain training adherence when using the InterWalk app during a 12-week period in patients with T2D. Some uncertainty about the effect size of adherence remains.
Data from: The effect of Speed of Processing training on microsaccade magnitude
Older adults experience cognitive deficits that can lead to driving errors and a loss of mobility. Fortunately, some of these deficits can be ameliorated with targeted interventions which improve the speed and accuracy of simultaneous attention to a central and a peripheral stimulus called Speed of Processing training. To date, the mechanisms behind this effective training are unknown. We hypothesized that one potential mechanism underlying this training is a change in distribution of eye movements of different amplitudes. Microsaccades are small amplitude eye movements made when fixating on a stimulus, and are thought to counteract the "visual fading" that occurs when static stimuli are presented. Due to retinal anatomy, larger microsaccadic eye movements are needed to move a peripheral stimulus between receptive fields and counteract visual fading. Alternatively, larger microsaccades may decrease performance due to neural suppression. Because larger microsaccades could aid or hinder peripheral vision, we examine the distribution of microsaccades during stimulus presentation. Our results indicate that there is no statistically significant change in the proportion of large amplitude microsaccades during a Useful Field of View-like task after training in a small sample of older adults. Speed of Processing training does not appear to result in changes in microsaccade amplitude, suggesting that the mechanism underlying Speed of Processing training is unlikely to rely on microsaccades.
Data from: Ecological immunization: In situ training of free-ranging predatory lizards reduces their vulnerability to invasive toxic prey
In Australia, large native predators are fatally poisoned when they ingest invasive cane toads (Rhinella marina). As a result, the spread of cane toads has caused catastrophic population declines in these predators. Immediately prior to the arrival of toads at a floodplain in the Kimberley region, we induced conditioned taste aversion in free-ranging varanid lizards (Varanus panoptes), by offering them small cane toads. By the end of the 18-month study, only one of 31 untrained lizards had survived longer than 110 days, compared to more than half (nine of 16) of trained lizards; the maximum known survival of a trained lizard in the presence of toads was 482 days. In situ aversion training (releasing small toads in advance of the main invasion front) offers a logistically simple and feasible way to buffer the impact of invasive toads on apex predators.
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.