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ShareScore release 0.9.0
Dataset results
16 results for “DIY”
Dataset: Mask R-CNN Based C. Elegans Detection with a DIY Microscope
<p>The dataset consists of images of C. elegans in Petri Dish that were captured at a frequency of 1 Hz at 3280 × 2464 pixels via a Raspberry Pi based DIY Microscope. Further details of the recording setup and the dataset can be found in the corresponding article.</p> <p>Up on use, please cite the following article <a href="https://doi.org/10.3390/bios11080257">https://doi.org/10.3390/bios11080257</a> such as:</p> <p>Fudickar, S.; Nustede, E.J.; Dreyer, E.; Bornhorst, J. Mask R-CNN Based C. Elegans Detection with a DIY Microscope. <em>Biosensors</em> <strong>2021</strong>, <em>11</em>, 257. https://doi.org/10.3390/bios11080257</p> <p> </p> <p><br> </p>
CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription
<p>We collect and release CrowdSpeech — the first publicly available large-scale dataset of crowdsourced audio transcriptions. e show its applicability on an under-resourced language by constructing VoxDIY — a counterpart of CrowdSpeech for the Russian language.</p>
DIY Particle Detector: reference measurement data
<p>This dataset is released along the data analysis source code of <a href="https://github.com/ozel/DIY_particle_detector">github.com/ozel/DIY_particle_detector</a> and <a href="https://doi.org/10.5281/zenodo.3361755">10.5281/zenodo.3361755</a>.</p> <p>Since the binary files are large, they are not part of the github archive (but stored via github's LFS feature for large files).</p> <p>The files are in python's pickle format, created using python version 3.6.5, pandas module version 0.24.1 and numpy module version 1.14.3. </p> <p>The measurements are taken with the diode-based detector design detailed in the repository above besides the following two files, which are recorded with iPadPix (<a href="https://doi.org/10.1088/1748-0221/11/11/C11032">https://doi.org/10.1088/1748-0221/11/11/C11032</a>) for comparison:<br> 3hoursRadonBalloon_2019-02-10_14-43-21___2321___2-56.pkl<br> KCL_block_bare_2019-02-11_20-54-41___1083___1-03.pkl</p> <p> </p> <p> </p> <p> </p>
DiY Sensor Dataset for Air Pollution Monitoring
<p>The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.</p>
Appendix for "Don't DIY: Automatically transform legacy Python code to support structural pattern matching"
<p>This is the appendix for paper "Don’t DIY: Automatically transform legacy Python code to support structural pattern matching" presented in SCAM 2022.</p> <p><strong>Abstract</strong></p> <p><em>As data becomes more and more complex as technology evolves, the need to support more complex data types in programming languages has grown. However, without proper storage and manipulation capabilities, handling such data can result in hard-to-read, difficult-to-maintain code. Therefore, programming languages continuously evolve to provide more and more ways to handle complex data. Python 3.10 introduced structural pattern matching, which serves this exact purpose: we can split complex data into relevant parts by examining its structure, and store them for later processing. Previously, we could only use the traditional conditional branching, which could have led to long chains of nested conditionals. Maintaining such code fragments can be cumbersome. In this paper, we present a complete framework to solve the aforementioned problem. Our software is capable of examining Python source code and transforming relevant conditionals into structural pattern matching. Moreover, it is able to handle nested conditionals and it is also easily extensible, thus the set of possible transformations can be easily increased.</em></p>
Image dataset for training of an insect detection model for the Insect Detect DIY camera trap
<p>This dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect DIY camera trap</a>, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).</p><h2>Classes</h2><p>The following object classes were annotated in this dataset:</p><ul><li><strong>wasp</strong> (mostly <i>Vespula</i> sp.)</li><li><strong>hbee</strong> (<i>Apis mellifera</i>)</li><li><strong>fly</strong> (mostly Brachycera)</li><li><strong>hovfly</strong> (various Syrphidae, e.g. <i>Episyrphus balteatus</i>)</li><li><strong>other</strong> (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)</li><li><strong>shadow</strong> (shadows of the recorded insects)</li></ul><p>View the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/health">Health Check</a> for more info on class balance.</p><h2>Versions</h2><ul><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/4">v4 insect_detect_416_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 416x416 pixel</li><li>all classes merged into one class ("insect")</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/5">v5 insect_detect_raw_4K</a><ul><li>original images in 4K resolution (3840x2160 pixel)</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/7">v7 insect_detect_320_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 320x320 pixel</li><li>all classes merged into one class ("insect")</li></ul></li></ul><h2>Deployment</h2><p>You can use this dataset as starting point to train your own insect detection models. Check the <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_detection/">model training instructions</a> for more information.</p><p>Open source Python scripts to deploy the trained models can be found at the <a href="https://github.com/maxsitt/insect-detect">insect-detect GitHub repo</a>.</p>
DIY-Concert Ensemble Vortex- Works by Rama Gottfried, Alessandro Perini and David Bird
<p>Hybrid Performance by Ensemble Vortex of the following works: </p> <p>- Apophänie. 2017. Video-Puppetry-Instrument, by Rama Gottfried (00:00-11:13)</p> <p>- Three Studies for Two Voices. 2017. Two performers, by Alessandro Perini (11:14-20:08)</p> <p>- Dark Ethnography. 2020. Four flashlight operators and cello, by David Bird (20:23-33:29) </p>
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities' drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall.
DIY chemical space product file
<p>The chemical space made up from 1000 building blocks using Mcule's ARCHIE.</p>
Do It Yourself (DIY) Coffee Study; Test Effect of Coffee on Cognition in an at Home Setting
ClinicalTrials.gov study NCT02061982. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
Open the record for dataset details and reuse information.
DIY Modifications for Transparent Headphones
<p>Accompanying data for AES ebrief "DIY Modifications for Very Open Headphones". HRTF measurement of KEMAR dummy head without a headphone, the AKG K702 and a modified version thereof. Measured in MCC at Aalto Acoustics Lab. High resolution only in the horizontal plane.</p>
Performance Evaluation of DIY Digital Visual Acuity Test
ClinicalTrials.gov study NCT07331870. IPD Sharing: NO. Countries: 1. Publications: 0.
AI-Powered DIY Screening System for Diabetic Retinopathy
ClinicalTrials.gov study NCT06892353. IPD Sharing: NO. Countries: 1. Publications: 0.
DIY houses at Philips
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652274621.0755.jpg">https://4dcity.org/imgupload/1652274621.0755.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F657391%2FZelfbouw_bij_Philips_%25281_02%2529.png&type=VIDEO">https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F657391%2FZelfbouw_bij_Philips_%25281_02%2529.png&type=VIDEO</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652274621.0755.jpg<br>Image Dimensions: 360x288<br>Megapixels: 0.10 MP<br>Filesize: 59.84 KB<br>
Need more #strings, do #ya? #lapslide #lapsteel #deliria #666 #brandnew #weapon #diy #Luther #homegrown #shaka #ernieball #tunings #guitar #onepiece #obeyyourmaster Whataniceride.co.nr
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1656262187.4341.jpg">https://4dcity.org/imgupload/1656262187.4341.jpg</a> <br>
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.