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1,399 results for “manual”
Moinho manual/ Hand mill
Instrumentos de pedra utilizados para triturar e moer grão fazendo uso exclusivo de força manual. A peça inferior, que se mantém estática, designa-se por dormente. A peça mais pequena - designada de movente - seria friccionada, manualmente, sobre o dormente. Entre as duas era colocado o material que se pretendia moer. Esta tecnologia surge durante o Neolítico, sendo utilizada ao longo da pré-história. Stone device used to crush and grind grain using only manual force. The lower piece remains static, while the smallest piece would be manually rubbed over it. The material to be milled was placed between the two. This technology appears during the Neolithic, being used throughout prehistory. ÉPOCA/PERIOD Neolítico/Neolithic PROVENIÊNCIA/SOURCE Dormente - [Anta Maior de Pedralta](http://u.osmfr.org/m/543623/#19/40.78543/-7.82703) (Cota, Viseu) ACERVO/COLLECTION Coleção Arqueológica José Coelho, s/no Source: Objaverse 1.0 / Sketchfab
The workload of manual data entry for integration between mobile health applications and eHealth infrastructure
<div> <p>In this study, we conducted a time-motion study observing healthcare workers (HCWs) completing data management activities including monitoring and evaluation (M&E) and manual data linkage of individual-level app data to electronic medical records (EMRS). This study served as a baseline study for an open-source app to mirror EMRS and reduce HCW workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi.</p> </div>
Extracts from Boyd et al., 2022, LIWC Manual
Open the record for dataset details and reuse information.
Pancreas-CT-SEG: DICOM-converted manual segmentations of pancreas for the Pancreas-CT collection
<p>This dataset contributes DICOM-converted annotations of the pancreas to the publicly available National Cancer Institute Imaging Data Commons [1] Pancreas-CT collection (<a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=pancreas_ct">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=pancreas_ct</a>). The Pancreas-CT [2] collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 80 manual segmentations of the pancreas in the NIFTI format. Converting these NIFTI manual annotations to DICOM allows users to visualize the CT images and corresponding pancreas annotations in public DICOM-based viewers. The DICOM segmentation format includes additional metadata describing the contained segmented structure using standard SNOMED-CT terminology, and allowing users to easily map them to the reference DICOM CT images. The converted DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.</p> <p>To visualize and explore segmentations in this collection use this link to open it in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pancreas-CT-SEG" target="_blank" rel="noopener">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pancreas-CT-SEG</a>.</p> <p>Conversion from NIFTI to DICOM SEG modality was performed using the dcmqi toolkit (https://github.com/QIICR/dcmqi)[4]. Resulting objects were validated using dicom3tools dciodvfy (<a href="https://www.dclunie.com/dicom3tools.html">https://www.dclunie.com/dicom3tools.html</a>). Details describing the conversion process are provided in this GitHub repository: <a href="https://github.com/ImagingDataCommons/idc-dicom-seg-conversions" target="_blank" rel="noopener">https://github.com/ImagingDataCommons/idc-dicom-seg-conversions</a>.</p>
Manually annotated RNA-focused scientific articles -- Experimental dataset
<p>In this repository, we store 100 <a href="../records/11393776/files/annotations.json?download=1">paragraphs sourced from RNA-focused scientific articles that have been manually annotated</a> (both for entities and relations) according to a <a href="../records/11393776/files/RNA-KG.yaml?download=1">LinkML template</a> that represent associations among genes, proteins, RNAs, chemicals, variants (SNPs), GO terms, and diseases. The template reflects a <a href="../records/11393776/files/meta-graph.pdf?download=1">subportion of RNA-KG's meta-graph</a>. <a href="https://doi.org/10.48550/arXiv.2312.00183">RNA-KG</a> data are available at: <a href="../records/10078876">https://zenodo.org/records/10078876</a>.</p>
Manual Dependency Annotation of Three German Text Extracts from the Project hermA (Gold Standard Data)
<p>This dataset was created in the digital humanities project hermA (www.herma.uni-hamburg.de) and comprises annotated extracts of the following three texts:</p> <ul> <li>Modern literature (Lit2009): novel <em>Corpus Delicti: Ein Prozess </em>by German author Juli Zeh, published in Frankfurt/Main in 2009.</li> <li>Non-contemporary literature (Lit1850):<em> Eine Frauenfahrt um die Welt </em>('A woman’s journey around the world') by Austrian author Ida Pfeiffer (1850). Full text available at Deutsches Textarchiv: http://www.deutschestextarchiv.de/pfeiffer_frauenfahrt01_1850/6.</li> <li>Modern academic writing (Aca2009): <em>Stand, Möglichkeiten und Grenzen der Telemedizin in Deutschland</em> ('Telemedicine in Germany: status, chances and limits') by Rüdiger Klar and Ernst Pelikan, published in Bundesgesundheitsblatt ('Federal Health Gazette') in 2009. DOI 10.1007/s00103-009-0787-7.</li> </ul> <p>The texts are annotated for part-of-speech and dependency syntax and are made available in CoNLL file format. We describe the annotation process and report inter-annotator agreements in:</p> <p>Adelmann, Benedikt, Melanie Andresen, Wolfgang Menzel & Heike Zinsmeister. 2018. Evaluation of Out-Of Domain Dependency Parsing for its Application in a Digital Humanities Project. <em>Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018)</em>. Vienna, Austria.</p>
Dataset with manually validated version histories of Stack Overflow posts
<p>We used this dataset to evaluate different string similarity metrics for SOTorrent (http://sotorrent.org/). For the versions published 2018-11-01 and 2018-12-14, we double-checked and updated the ground truth files.</p> <p>The dataset has been created with this tool: https://github.com/sotorrent/posthistory-gt</p> <p>The dataset has been validated with this tool: https://github.com/sotorrent/posthistory-comparator-gt-cs</p> <p>The dataset has been used in this project: https://github.com/sotorrent/metric-evaluation</p> <p>The most recent version of the files can always be found here: https://github.com/sotorrent/metric-evaluation/tree/master/testdata/samples_comparison</p>
Figure 13 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 13. Sponge types which can be used for nematode storage and formulation.
Figure 2 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 2. Endotokia matricida stage of an entomopathogenic nematode.
Figure 12 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 12. Setup of infectivity bioassay in 24-well plates with tape to prevent escape of insects.
Figure 1 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 1. Life cycle of entomopathogenic nematode/bacteria complex in a lepidopteran insect.
Figure 10 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 10. Isolation of Xenorhabdus and Photorhabdus from Galleria mellonella hemolymph.
Figure 4 in Basic laboratory and field manual for conducting research with the entomopathogenic nematodes, Steinernema and Heterorhabditis, and their bacterial symbionts
Figure 4. Steps in isolating entomopathogenic nematodes from soil.
Reference Spectra, Laboratory Mixture Spectra, list of NIMS cubes used in this study and the manual corrections to these NIMS cubes for better alignment.
<p>Reflectance spectra for the endmember library, including both the public data and data made in this study. <br><br>Reflectance spectra for the 100% SAO, 10% SAO, 25% SAO, 80% SAO, and 100% Water ice mixtures.<br><br>Text File of the PDS IDs of NIMS data cubes analyzed in this study.</p> <p>(X,Y) offsets applied to each data cube for better alignment between NIMS data and the Global Mosaic.</p>
Dump truck object detection with manual annotations
<p>Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are:</p> <p>0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap</p> <p>This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Figure 10 in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 10. Left manus of Compsognathus longipes (holotype), articulated by camera lucida.
Figure 9 in On the manual morphology of Compsognathus longipes and its bearing on the diagnosis of Compsognathidae
Figure 9. Isolated elements that may be referable to first phalanges of digit III.
Manual tumor annotations in TCGA
<p><strong>What is this</strong></p> <p>These are manual annotations of tumor tissue on TCGA diagnostic whole slide images in major solid tumor types. The aim of this project was to enrich for regions with invasive tumor tissue for subsequent molecular prediction studies, excluding whitespace, artifacts and non-tumor tissue as efficiently as possible. The aim was not to create a perfect tumor annotation on the pixel level. Annotations were done by trained observers using QuPath v0.1.2 and were converted to CSV. "COAD" and "READ" were merged to "CRC".</p> <p><strong>More resources</strong></p> <ul> <li>For difference between diagnostic and frozen slides, please see: <a href="http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/">http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</a></li> <li>For a list of all tumor types in TCGA, see, see: <a href="https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations">https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations</a></li> </ul> <p><strong>Legal</strong></p> <p>No guarantees, no liability. </p>
Manual 4D annotations of Micro X-ray CT time-series (4D dataset)
<p>The 4D (3D+time) manual annotations of https://doi.org/10.5281/zenodo.4293394. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547) and our proposed hidden Markov model (HMM-T, https://doi.org/10.5281/zenodo.4416013 ) designed to refine 4D semantic segmentations made by a 3D semantic segmentation CNN after its applied on 4D data. Only slices 740-742 and 747-749 (refining to the first axis) are partially annotated. We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</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.