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1,399 results for “manual”
Comparing gene expression of human cardiac stem cells between automatic cell culture and manual handling culture
GEO Series GSE44032. Homo sapiens. 4 samples. Type: Expression profiling by array.
Deep-sequencing of total RNA from manually dissected wild-type Arabidopsis embryos at the globular stage
GEO Series GSE33866. Arabidopsis thaliana. 1 samples. Type: Expression profiling by high throughput sequencing.
YOLOv8 Manual and MATT Model Weights for RGB, LWIR, and RGB-LWIR Images
Open the record for dataset details and reuse information.
Manually Labeled RGB, LWIR, and RGB-LWIR Images of Cars and Trucks for Multispectral Automated Transfer Technique (MATT) Research
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[DEPRECATED] A manually-curated categorisation of Java Maven libraries along Python PyPI Topics (dataset)
<p>This dataset has been superseded by <strong><a title="https://zenodo.org/records/10480832" href="../records/10480832">https://zenodo.org/records/10480832</a></strong></p> <p> </p> <p>-- Test edit (new version?)</p>
VETO: Vessel topology manual annotation dataset
<p>We have labeled the vessel topology on four public-available retinal datasets:</p> <p>INSPIRE [1]<br>VICAVR [2]<br>IOSTAR [3]<br>DRIVE [4]<br>Two experts were asked to manually label the topological information of the retinal vascular structure by using a graph editing software we developed for this task. Expert one and two independently labeled each vessel segment or centerline for all datasets, based on the types of available manual annotations of the vessel structure. Then the consensus between them was released for public use.</p> <p>It is worth noting that the vessel segments or vessel centerlines were used for topology estimation were extracted either by human grader or automatic vessel segmentation method, i.e. the DRIVE and IOSTAR datasets include the manual annotations of retinal vessel for each image, so the topology reconstruction were performed at manual annotated vessel patterns; for VICAVR datasets, the topology estimation were performed at the automatic segmented vessels by using the automated segmentation method [5]; for INSPIRE dataset, the human expert graded the topology at the vessel centerline which were provided by [6].</p> <p>[1] M. Niemeijer, X. Xu, A. Dumitrescu, B. van Ginneken, J. Folk, and M. Abràmoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med.Imaging, vol. 30, no. 11, pp. 1941–1950, 2011.</p> <p>[2] S. G. Vázquez, B. Cancela, N. Barreira, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl., vol. 24, no. 5, pp. 919– 930, 2013.</p> <p>[3] J. Zhang, B. Dashtbozorg, E. J. Bekkers, J. P. W. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging, vol. 35, pp. 2631–2644, 2016.</p> <p>[4] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, pp. 501–509, 2004.</p> <p>[5] Y. Zhao, L. Rada, K. Chen, , and Y. Zheng, “Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images,” IEEE Trans. Med. Imaging, vol. 34, no. 9, pp. 1797–1807, 2015.</p> <p>[6] R. Estrada, C. Tomasi, S. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1688–1701, 2015.</p>
Single-cell RNA sequencing of 76,535 capillary PBMCs from 3 donors across 28 samples, freshly isolated or on ice for 24h with manual 4-layer annotation hierarchy
<p><strong>Sample Procurement</strong></p> <p>Samples were collected directly from participants as part of ImYoo's "Single-cell immune profiling from self-collected capillary blood" study, approved by Advarra IRB (Protocol #Pro00057361).</p> <p><strong>Sample Processing</strong></p> <p>Whole capillary blood samples were self-collected from participants using the <a href="https://yourbiohealth.com/en-us/virtually-painless-blood-collection-devices-for-clinical-trials-and-wellness-testing">TAP II</a> device. Samples were either processed shortly after collection, or if being stored for longer than 4 hours, were kept in a styrofoam cooler with ice packs. Cells were isolated using <a href="https://www.stemcell.com/products/easysep-direct-human-pbmc-isolation-kit.html">EasySep Direct Human PBMC Isolation Kit</a> (STEMCELL Technologies Catalog #19654) and cryopreserved using <a href="https://www.stemcell.com/cryostor-cs10.html">CryoStor CS10</a> (STEMCELL Technologies Catalog #07930). Upon thawing, samples were labeled in accordance with the MULTI-seq protocol (<a href="https://www.nature.com/articles/s41592-019-0433-8">https://www.nature.com/articles/s41592-019-0433-8</a>) and then processed on a 10X Genomics Chromium, using either the Chromium Next GEM Single Cell 3' Kit v3.1 (10X Genomics Product Code 1000269) or Chromium Next GEM Single Cell 3’ HT Kit v3.1 (10X Genomics Product Code 1000370). DNA libraries were sequenced on either a NovaSeq 6000 or NextSeq 550.</p> <p><strong>Data Processing</strong></p> <p>Transcriptomic sequencing data was processed using Cell Ranger v7.0.1 with default parameters. Multiplexing oligo sequencing data was processed through a custom python script that counts the number of occurrences of each sample barcode sequence and assigns it to the corresponding cell barcode. Samples were demultiplexed using a custom algorithm that estimates the background sample barcode counts, and assigns each cell a probability of belonging to each sample. Cell typing was done as part of a larger dataset and consisted of iterative manual assignments of clusters to cell types. For each cell subtype detected, a new model was trained on just the cells of that type, and the process was repeated.</p> <p><strong>Metadata Fields</strong></p> <ul> <li><strong>barcode:</strong> Original chromium cell barcode</li> <li><strong>Sample IDs:</strong> Unique ID for the experimental sample that was processed with 10x Chromium, could have come from the same biological sample (identified by <strong>original_sample_id</strong>)</li> <li><strong>Participant IDs:</strong> Unique ID for participant (here there are three participants: 2, 3 and 51)</li> <li><strong>Cell Barcoding Runs:</strong> Unique ID for the 10x Chromium cell barcoding run in which that sample was processed. Multiple samples can be processed in a cell barcoding run.</li> <li><strong>Lane:</strong> ID of which Chromium chip lane the cell came from</li> <li><strong>extraction_protocol:</strong> How the PBMCs were isolated from whole blood. In this dataset all samples were processed with the TAP device.</li> <li><strong>sample_processing_delay_seconds:</strong> The amount of time (in seconds) between when the blood was extracted from the participant and when PBMC isolation + cryopreservation was performed</li> <li><strong>cell_barcoding_delay_days:</strong> How long PBMC samples were stored in liquid nitrogen prior to being thawed and processed on 10x</li> <li><strong>cell_barcoding_protocol</strong>: Which single cell RNA sequencing experimental protocol was used. Here all samples were processed with 10x v3.1 chemistry.</li> <li><strong>run_lane_batch:</strong> Concatenation of columns <strong>Cell Barcoding Runs</strong> and <strong>Lane</strong> to provide a unique ID for experimental processing batch (i.e. the DNA library)</li> <li><strong>cell_type_level_1:</strong> Level 1 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_2:</strong> Level 2 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_3:</strong> Level 3 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_4:</strong> Level 4 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>c1:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c2:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c3:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c4:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>original_sample_id:</strong> Some samples were derived from the same originating whole blood sample. This field specifies the source of the whole blood sample.</li> </ul>
Monochromatic Digital-Pupillometry With Two Automated Devices in Comparison to Manual Measurements on Digital Photographs
ClinicalTrials.gov study NCT02978092. IPD Sharing: Not stated. Countries: 0. Publications: 0.
RNA-seq of manually sorted transgenically labeled sNPFr-Gal4 and TRH-Gal4 drosphila neurons
GEO Series GSE131405. Drosophila melanogaster. 13 samples. Type: Expression profiling by high throughput sequencing.
Repetitive in vivo manual loading of the spine elicits cellular responses in porcine annuli fibrosi
GEO Series GSE166656. Sus scrofa. 12 samples. Type: Expression profiling by high throughput sequencing.
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" 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 associated 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><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> 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 associated 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>
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" 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>
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>
Manually annotated video frames of bumblebees in flying arena
<p>Bounding boxes of</p> <ul> <li>Blue flowers</li> <li>Yellow flowers</li> <li>Bumblebees</li> <li>Bumblebees on flowers</li> </ul> <p>Annotated using CVAT.</p>
A practical guide to manual and semi-automated neurosurgical brain lesion segmentation
Open the record for dataset details and reuse information.
TONAS: a dataset of flamenco a cappella sung melodies with corresponding manual transcriptions
<p>This dataset contains a music collection of 72 sung excerpts representative of three a cappella singing styles (Deblas, two variants of Martinete).<br> The distribution is as follows:</p> <ul> <li>16 Deblas</li> <li>36 Martinete 1</li> <li>20 Martinete 2</li> </ul> <p>This collection was built in the context of a study on similarity and style classification of flamenco a cappella singing styles (Tonas) by the flamenco expert <a href="http://barbacana.net/mora/">Dr. Joaquin Mora</a>, Universidad de Sevilla.</p> <p>We refer to (<a href="http://mtg.upf.edu/node/1708">Mora et al. 2010</a>) for a comprehensive description of the considered styles and their musical characteristics. All 72 excerpts are monophonic, their average duration is 30 seconds and there is enough variability for a proper evaluation of our methods, including a variety of singers, recording conditions, presence of percussion, clapping, background voices and noise.</p> <p>We also provide manual melodic transcriptions, generated by the COFLA team and <strong>Cristina López</strong> <strong>Gómez.</strong></p> <p><strong>Using this dataset </strong></p> <p>When TONAS is used for academic research, we would highly appreciate if scientific publications of works partly based on the TONAS dataset quote the following publication:</p> <p>Music material</p> <blockquote> <p>Mora, J., Gomez, F., Gomez, E., Escobar-Borrego, F.J., Diaz-Banez, J.M. (2010). <a href="http://mtg.upf.edu/node/1708">Melodic Characterization and Similarity in A Cappella Flamenco Cantes</a>. 11th International Society for Music Information Retrieval Conference (ISMIR 2010).</p> </blockquote> <p>Transcriptions</p> <blockquote> <p>Gomez, E., Bonada, J. (in Press). <a href="http://mtg.upf.edu/node/2656">Towards Computer-Assisted Flamenco Transcription: An Experimental Comparison of Automatic Transcription Algorithms As Applied to A Cappella Singing</a>. Computer Music Journal.</p> </blockquote> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/tonas">https://www.upf.edu/web/mtg/tonas</a></p>
Psoas Cross-Sectional Measurements Using Manual CT Segmentation before and after Endovascular Aortic Repair (EVAR).
<p>Monti CB, Righini P, Bonanno MC, Capra D, Mazzaccaro D, Giannetta M, Nicolino GM, Nano G, Sardanelli F, Marrocco-Trischitta MM, Secchi F. Psoas Cross-Sectional Measurements Using Manual CT Segmentation before and after Endovascular Aortic Repair (EVAR). J Clin Med. 2022 Jul 12;11(14):4023. doi: 10.3390/jcm11144023. PMID: 35887786; PMCID: PMC9325160.</p> <p>Abstract</p> <p>Sarcopenia has been associated with an increased incidence of adverse outcomes, including higher mortality, after endovascular aortic repair (EVAR). We aim to use computed tomography (CT) to quantify changes in total psoas muscles area (PMA) and psoas muscle density (PMD) after EVAR, and to evaluate the reproducibility of both measurements. PMA and PMD were assessed via manual segmentation of the psoas muscle on pre- and post-operative CT scans belonging to consecutive patients who underwent EVAR. Wilcoxon test was used to compare PMA and PMD before and after EVAR, and inter- and intra-reader agreements of both methods were evaluated through Bland-Altman analysis. A total of 50 patients, 42 of them males (84%), were included in the study. PMA changes from 1243 mm<sup>2</sup> (1006-1445 mm<sup>2</sup>) to 1102 mm<sup>2</sup> (IQR 937-1331 mm<sup>2</sup>), after EVAR (<em>p</em> &lt; 0.001). PMD did not vary between pre-EVAR (33 HU, IQR 26.5-38.7 HU) and post-EVAR (32 HU, IQR 26-37 HU, <em>p</em> = 0.630). At inter-reader Bland-Altman analysis, PMA showed a bias of 64.0 mm<sup>2</sup> and a coefficient of repeatability (CoR) of 359.2 mm<sup>2</sup>, whereas PMD showed a bias of -2.43 HU and a CoR of 6.19 HU. At intra-reader Bland-Altman analysis, PMA showed a bias of -81.1 mm<sup>2</sup> and a CoR of 394.6 mm<sup>2</sup>, whereas PMD showed a bias of 1.41 HU and a CoR of 6.36 HU. In conclusion, PMA decreases after EVAR. A good intra and inter-reader reproducibility was observed for both PMA and PMD. We thus propose to use PMA during the follow-up of patients who underwent EVAR to monitor muscle depletion after surgery.</p>
Pre-TAVI aortic annulus sizing: Comparison between manual and semi-automated new generation software measurements in operators with different experience
<p>The study compares manual and semi-automatic measurements (with specific dedicated GE Software) for aortic annulus assessment among different operators with different experience</p>
UPOV images\Manually segmented
<p>Manually segmented images from the UPOV (International Union for the Protection of New Varieties of Plants) <em>Description of Components and Varieties of Sunflower</em> (GEVES, 2000) for determination of ray florets color.</p> <p>When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\UPOV images\Manually segmented.</p>
ScienceDex guides
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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.