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1,399 results for “manual”
SGS-LTER Standard Met Data: 1969- 2010 Manually Collected Standard Meteorological Data on the Central Plains Experimental Range, Nunn, Colorado, USA 1969 - 2011, ARS Study Number 6
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. additional information and referenced materials can be found:http://hdl.handle.net/10217/82146.
SGS-LTER Standard Met Data: 1969-2010 Manually Collected Aboveground Meteorological Data: English Units on the Central Plains Experimental Range, Nunn, Colorado, USA, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82446.
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</li> </ul> <p> </p> <p><strong>3). project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Dataset and manual counts used in "Digging roots is easier with AI"
<p>Please see the paper "Digging roots is easier with AI" how the images were captured and the manual counting was performed for the three destructive sampling procedures. </p>
MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present
<p>A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).</p>
Crossing hands behind your back reduces recall of manual action sentences and alters brain dynamics
<p>The experiment involved a 2 Verb (manual, attentional) x 2 Hand posture (front, behind) within-participant design. Half of the participants were assigned randomly to one of the two sets of 120 experimental sentences. The sentences were divided into 12 experimental blocks of 11 sentences each: 5 manual and 5 attentional sentences as experimental materials, and 1 filler sentence that was added at the beginning of each block to discard recall primacy effects and excluded from the analysis. An additional block of 5 sentences was used as practice. Each experimental block consisted of: a) learning phase, including the 10 experimental sentences in random order, plus the initial filler sentence, b) one-minute distractive task, c) recall phase.</p> <p>For more information about the data, please refer to the Corresponding Author</p>
Training of 7-month-old infants' manual object exploration skills: Effects of active and observational experience
<p>Dataset representing the data of the paper "Training of 7-month-old infants' manual object exploration skills: Effects of active and observational experience."</p>
Eastern Canada Flocks: Images and manually annotated bird positions
The Eastern Canada (ECA) Flocks data set consists of manually annotated Images from the Common Eider (COEI, Somateria mollissima) Winter Survey and the Greater Snow Geese (GSGO, Anser caerulescens atlanticus) Spring Survey. The images were taken in Eastern Canada using fixed-wing aircraft and manually annotated with ImageJ's Cell counter plugins. We selected and annotated the ECA Flocks images in order to test the precision of the CountEm flock size estimation method. ECA Flocks includes 179 COEI and 99 GSGO single flock images. We cut each image manually to a rectangle that excluded large parts of the image with no birds. Both versions (original and cut) of each image are available in the data set. We manually annotated 637,555 (124,309 COEI and 514,235 GSGO) bird positions in the cut images from both surveys. Each bird has an associated "Type" which refers to species and/or sex. Sex identification was only possible for adult common eiders since females and immature males are brown birds whereas adult males have mainly white plumage. 64,484 male and 58,029 females were identified in the COEI images, as well as 1796 birds of other species. 504,891 Snow Geese and 9344 birds of other species were labeled in the GSGO images. A .csv file including all annotated bird positions and types is available for each image. The COEI and GSGO photos of the ECA Flocks data set were taken in the years 2006 and 2018 and 2016-2018 respectively. We selected these photos in order to include images with different quality and resolution. COEI and GSGO flock sizes range from 6 to 4,154 and from 43 to 36, 241 respectively. There is high variability in light conditions, backgrounds, number and spatial arrangement of birds across the images. The data set is therefore potentially useful to test the precision of methods for analyzing imagery to estimate the abundance of animals by directly detecting, identifying and counting individuals.
Reconfiguring motor circuits for a joint manual and BCI task
<p>SQL database containing all models and analysis results for listed publication. Results and figures from paper can be reproduced by using the code at https://github.com/benlansdell/dualbci</p>
Korsnes museum, Manual coffee grinder
https://www.museumnord.no/en/our-venues/korsnes-museum/ My 3D reconstruction generated with photogrammetry software 3DF Zephyr v6.507 processing 132 images Source: Objaverse 1.0 / Sketchfab
All Tasks- STROKE - ALAMEDA Bracelet Data with manual Annotations
<p>The tasks_all_total_with_manual_annotations.csv file contains accelerometer data and task labels of the pilot stroke patients that are given in folder datasets/bracelet/<strong>stroke</strong>. It includes 11 patients that are manually annotated and consists of 7 columns. Those columns are:</p> <ol> <li> <p>x, y, z, which represent the accelerometer values of the bracelets sensors used on either left or right wrist of the patients</p> </li> <li> <p>T and time, which represent the timestamp of the activity (time) and the period (T).</p> </li> <li> <p>Patient ID column, which is the number id of the patients.</p> </li> <li> <p>Task column, which represents the task performed by the patient.</p> </li> </ol> <p>This file contains the tasks that are described below. Inside of each parenthesis, is given the name of each task, based on the annotations that were provided on datasets/annotations/raw_medical_tracking/stroke/<strong> intense-monitoring_with_manual_annotations_v2.xlsx</strong> file and on the accelerometer data that were available on stroke pilot folder mentioned above. Those tasks are:</p> <ol> <ol> <li> <p>cane_above_head (Cane above the head)</p> </li> <li> <p>standing_on_forefeet (Standing on the forefeet)</p> </li> <li> <p>lateral_steps (Lateral steps)</p> </li> <li> <p>rotation_cane (Rotations using a cane)</p> </li> <li> <p>cane_to_chest (Cane-to-chest)</p> </li> <li> <p>lateral_movement (Lateral movements with cane)</p> </li> <li> <p>hands_on_cane (Hands on the cane)</p> </li> <li> <p>lifting_knees (Lifting the knees)</p> </li> <li> <p>normal_walk (Normal walking)</p> </li> <li> <p>tandem_walk (Tandem walking)</p> </li> <li> <p>bicycle_walk (Bycicle walking)</p> </li> <li> <p>walk_with_knees_raised (Walking with the knees raised)</p> </li> <li> <p>rowing_movement (Rowing movements)</p> </li> <li> <p>flexion_extension_knees (Flexion and extension of the knees)</p> </li> </ol> </ol> <p> </p> <p>The features used to recognize activities in stroke patients are x,y,z and Task.</p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Data, code, and manual for analysis of image heterogeneity in mass spectrometric imaging.
<p>This upload contains all replication material for "The software for interactive evaluation of mass spectrometric imaging heterogeneity" (forthcoming).</p> <p><strong>Authors:</strong> E.S. Zhvansky, E.V. Zhdanova, M.S. Belenikin, M.A. Shamraeva, S.V. Silkin, K.V. Bocharov, A.A. Sorokin.</p> <p><strong>Code, manual, and data are located within Interactive_CSMM.zip</strong><strong>.</strong> Code is written MATLAB R2019b and Python 3.5.2.</p> <p>Please find the README.md for code using and the code to replicate the main findings of the paper described below:</p> <ul> <li>imzml2mat.py in conversion directory for file conversion.</li> <li>CSMM.m for application start.</li> <li>40TopL,10TopR,30BottomL,20BottomR-centroid.mat - data in conversion/Data directory for reproducing the results.</li> <li>User_manual.pdf - manual.</li> <li>mp4 files - screencasts.</li> </ul>
Arrival-time data manually picked up of Pn wave in Hainan Island and surrounding areas
<p>This file is the arrival-time data manually picked up of Pn wave in Hainan Island and surrounding areas. It is only used for scientific research.</p>
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking
<p>Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.</p> <p><em>Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.</em></p> <p>Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a></p> <p><strong>This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:</strong></p> <p><a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a></p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at RO-MAN 2022 </p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
Manually mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains manually mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Mapping has been done in ArcGIS with snapping toggled on to document the topological abutment relationships between traces.</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in manual mapping was done as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the years 2019 and 2020.</p> <p>This dataset has been previously used as part of a Master's Thesis, which can be located here: <a href="https://urn.fi/URN:NBN:fi-fe202003259211">https://urn.fi/URN:NBN:fi-fe202003259211</a>, and in a Journal of Structural Geology article (OG1 target area), located here: https://doi.org/10.1016/j.jsg.2021.104304.</p>
Manually verified publication prediction dataset.
<p>Includes the clinical trial identifiers with publication status decisions sourced from three studies:</p> <p>- Joseph S Ross, Tony Tse, Deborah A Zarin, Hui Xu, Lei Zhou, and Harlan M Krumholz. Publication of NIH funded trials registered in clinicaltrials. gov: cross sectional analysis. BMJ, 344, 2012.</p> <p>- Adam G Dunn, Enrico Coiera, and Florence T Bourgeois. Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials. gov. Journal of clinical epidemiology 95: 94-101, 2018.</p> <p>- Deborah A Zarin, Tony Tse, Rebecca J Williams, Robert M Califf, and Nicholas C Ide. The ClinicalTrials. gov results database—update and key issues. New England Journal of Medicine 364, no. 9: 852-860, 2011.</p> <p>The authors of the studies have agreed with the publication of this dataset.<br> </p>
Automated and manual pooled sample testing with panther fusion and aptima SARS-CoV-2 assays
<p>Combining diagnostic specimens into pools has been considered as a strategy to augment throughput, decrease turnaround time, and leverage resources. This study utilized a multi-parametric approach to assess optimum pool size, impact of automation, and effect of nucleic acid amplification chemistries on the detection of SARS-CoV-2 RNA in pooled samples for surveillance testing on the Hologic Panther Fusion® System. Dorfman pooled testing was conducted with previously tested SARS-CoV-2 nasopharyngeal samples using Hologic's Aptima® and Panther Fusion® SARS-CoV-2 Emergency Use Authorization assays. A manual workflow was used to generate pool sizes of 5:1 (five samples: one positive, four negative) and 10:1. An automated workflow was used to generate pool sizes of 3:1, 4:1, 5:1, 8:1 and 10:1. The impact of pool size, pooling method, and assay chemistry on sensitivity, specificity, and lower limit of detection (LLOD) was evaluated. Both the Hologic Aptima® and Panther Fusion® SARS-CoV-2 assays demonstrated >85% positive percent agreement between neat testing and pool sizes ≤5:1, satisfying FDA recommendation. Discordant results between neat and pooled testing were more frequent for positive samples with CT>35. Fusion® CT (cycle threshold) values for pooled samples increased as expected for pool sizes of 5:1 (CT increase of 1.92 - 2.41) and 10:1 (CT increase of 3.03 - 3.29). The Fusion® assay demonstrated lower LLOD than the Aptima® assay for pooled testing (956 vs 1503 cp/mL, pool size of 5:1). Lowering the cut-off threshold of the Aptima® assay from 560 kRLU (manufacturer's setting) to 350 kRLU improved the assay sensitivity to that of the Fusion® assay for pooled testing. Both Hologic's SARS-CoV-2 assays met the FDA recommended guidelines for percent positive agreement (>85%) for pool sizes ≤5:1. Automated pooling increased test throughput and enabled automated sample tracking while requiring less labor. The Fusion® SARS-CoV-2 assay, which demonstrated a lower LLOD, may be more appropriate for surveillance testing.</p>
EXCEED-DMv1.0.0: User Manual Results for Si and Ge
<p>DM-electron interaction rates in Si and Ge targets used for the user manual: <a href="https://arxiv.org/abs/2210.14917">[2210.14917] EXCEED-DM: Extended Calculation of Electronic Excitations for Direct Detection of Dark Matter (arxiv.org)</a>. All plots in the user manual can be reproduced from the data here. File names which only differ by "_<number>" are the same calculation, split for different DM mass points.</p> <p> </p> <p>Calculations included/folder structure is:</p> <p>- (Si/Ge)/ - Calculations for Si/Ge targets.</p> <p> - scatter/ - Binned scattering rate calculations.</p> <p> - dark_photon/ - Kinetically mixed dark photon.</p> <p> - screening/ - Different approximations about f_scr, the screening factor</p> <p> - no_scr/ - No screening</p> <p> - ana_scr/ - Analytic screening</p> <p> - num_scr/ - Numeric screening</p> <p> - annual_modulation - Calculations for the annual modulation of the DM-electron scattering rate, screened with numerically computed dielectric.</p> <p> - abs/ - Absorption rate calculations</p> <p> - vector/ - vector DM</p> <p> - scalar/ - scalar DM</p> <p> - ps/ - pseudoscalar DM</p> <p> - dielectric/ - Numerically computed dielectric</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.