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11,687 results for “training”
ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence and transmitted light)</p> <p><strong>Microscopy data type</strong>: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)</p> <p><strong>Microscope</strong>: Leica SP8, HC PL APO 63x 1.40 NA oil objective </p> <p><strong>Cell type</strong>: HeLa (fixed using an organelle-preserving protocol)</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 x 32 (Pixel size: x and y: 90 nm pixel size, z: 150 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn</p> <p><strong>Contact email</strong>: c.spahn@chemie.uni-frankfurt.de, heilemann@chemie.uni-frankfurt.de</p> <p><strong>Affiliation</strong>: Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><br> <strong>Funding body(ies)</strong>: M.H. and C.S.: German Science Foundation (grant nr. SFB1177). C.S.: European Molecular Biology Organization (short term fellowship 8589)</p>
Training dataset for semantic segmentation (U-Net) of structural conservation practices
<p>In this research, the best management practices include vegetative/structural conservation practices (SCP) across crop fields, such as grassed waterways and terraces. This reference dataset includes 500,000 pair patches (false-color image (B1: NIR, B2: Red, B3: Green) and binary label (SCP: yes[1] or no[0]). These training samples were randomly extracted from Iowa BMP project (<a href="https://www.gis.iastate.edu/gisf/projects/conservation-practices">https://www.gis.iastate.edu/gisf/projects/conservation-practices</a>) and present 90% of patches with SCP areas and 10% of patches non-SCP area. The patch dimension is 256 x 256 pixels at 2-m resolution. Due to the file size, the images were upload in different *.rar files (imagem_0_200k.rar, imagem_200_400k.rar, imagem_400_500k.rar), and the user should download all and merge them in the same folder. The corresponding labels are all in "class_bin.rar" file.</p> <p>Application: These pair images are useful for conservation practitioners interested in the classification of vegetative/structural SCPs using deep-learning semantic segmentation methods.</p> <p>Further information will be available in future.</p>
MESINESP: Medical Semantic Indexing in Spanish - Train dataset
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p> </p> <p> </p> <p><strong>INTRODUCTION</strong>:</p> <p>The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records. </p> <p>The training dataset contains all records from LILACS and IBECS databases at the Virtual Health Library (VHL) with a non-empty abstract written in Spanish. The URL used to retrieve records is as follows:<br> http://pesquisa.bvsalud.org/portal/?output=xml&lang=es&sort=YEAR_DESC&format=abstract&filter[db][]=LILACS&filter[db][]=IBECS&q=&index=tw&</p> <p>We have filtered out empty abstracts and non-Spanish abstracts. </p> <p>The training dataset was crawled on 10/22/2019. This means that the data is a snapshot of that moment and that may change over time. In fact, it is very likely that the data will undergo minor changes as the different databases that make up LILACS and IBECS may add or modify the indexes.</p> <p> </p> <p><strong>ZIP STRUCTURE:</strong></p> <p>The training data sets contain 369,368 records from 26,609 different journals. Two different data sets are distributed as described below:</p> <p> - <em>Original Train set</em> with 369,368 records that also include the qualifiers, as retrieved from VHL. <br> - <em>Pre-processed Train set</em><strong> </strong>with the 318,658 records with at least one DeCS code and with no qualifiers. </p> <p> </p> <p> </p> <p><strong>STATISTICS</strong>:</p> <p>Abstracts’ length (measured in characters)<br> Min: 12<br> Avg: 1140.41<br> Median: 1094<br> Max: 9428</p> <p>Number of DeCS codes per file<br> Min: 1<br> Avg: 8.12<br> Median: 7<br> Max: 53</p> <p> </p> <p> </p> <p><strong>CORPUS FORMAT</strong>:</p> <p>The training data sets are distributed as a JSON file with the following format:</p> <pre><code>{ "articles": [ { "id": "Id of the article", "title": "Title of the article", "abstractText": "Content of the abstract", "journal": "Name of the journal", "year": 2018, "db": "Name of the database", "decsCodes": [ "code1", "code2", "code3" ] } ] } </code></pre> <p>Note that the decsCodes field lists the DeCs Ids assigned to a record in the source data. Since the original XML data contain descriptors (no codes), we provide a DeCs conversion table (https://temu.bsc.es/mesinesp/wp-content/uploads/2019/12/DeCS.2019.v5.tsv.zip) with:</p> <p> - DeCs codes<br> - Preferred descriptor (the label used in the European DeCs 2019 set)<br> - List of synonyms (the descriptors and synonyms from both European and Latin Spanish DeCs 2019 data sets, separated by pipes)</p> <p> </p> <p>For more details on the Latin and European Spanish DeCs codes see: http://decs.bvs.br and http://decses.bvsalud.org/ respectively.</p> <p>Please, cite: Krallinger M, Krithara A, Nentidis A, Paliouras G, Villegas M. BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering. InEuropean Conference on Information Retrieval 2020 Apr 14 (pp. 550-556). Springer, Cham.</p> <p> </p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance
<p>A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (<a href="https://arxiv.org/abs/2007.04816">https://arxiv.org/abs/2007.04816</a>). </p> <p>Citation:</p> <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>
Popularity Dataset for Online Stats Training
<p>This is a dataset used for the online stats training website (<a href="https://www.rensvandeschoot.com/tutorials/">https://www.rensvandeschoot.com/tutorials/</a>) and is based on the data used by <a href="https://doi.org/10.1016/j.adolescence.2009.12.004">van de Schoot, van der Velden, Boom, and Brugman (2010)</a>.</p> <p>The dataset is based on a study that investigates an association between popularity status and antisocial behavior from at-risk adolescents (n = 1491), where gender and ethnic background are moderators under the association. The study distinguished subgroups within the popular status group in terms of overt and covert antisocial behavior.For more information on the sample, instruments, methodology, and research context, we refer the interested readers to <a href="https://doi.org/10.1016/j.adolescence.2009.12.004">van de Schoot, van der Velden, Boom, and Brugman (2010)</a>.</p> <p> </p> <p>Variable name Description</p> <p>Respnr = Respondents’ number</p> <p>Dutch = Respondents’ ethnic background (0 = Dutch origin, 1 = non-Dutch origin)</p> <p>gender = Respondents’ gender (0 = boys, 1 = girls)</p> <p>sd = Adolescents’ socially desirable answering patterns</p> <p>covert = Covert antisocial behavior</p> <p>overt = Overt antisocial behavior</p>
Endless Foram, MD022508 and MD9712138 training datasets
<p>Training datasets for the paper "Automated analysis of foraminifera fossil records by image classification using a convolutional neural network"</p> <p>The Endless Forams dataset is a derivative work, and was created from the original dataset (endlessforams.org, <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003612">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003612</a>) by removing the text and border from each image.</p>
Glassware images and code samples for training and identification of glassware by neural networks
<p>These images were used to perform an image identification exercise with first-year students in the author's Introduction to Scientific Computing course. The zip file also includes sample Mathematica notebooks that were used to perform the training and data analysis of the neural network's performance. The corresponding publication in the Journal of Computational Science Education can be found here <a href="https://doi.org/10.22369/issn.2153-4136/12/1/2">https://doi.org/10.22369/issn.2153-4136/12/1/2</a></p>
Emergent Cricothyrotomy Training for Non-Surgeons
<p>The video "Emergent Cricothyrotomy Training for Non-Surgeons" reviews two distinct techniques for performing an emergency cricothyrotomy as taught by an otolaryngology surgeon utilizing cadaveric donors. The first method is employable in almost any emergency setting and uses a scalpel, a bougie, and a 6.0 endotracheal tube. The second method uses a cricothyrotomy kit and has a familiar Seldinger technique. The video also reviews more advanced techniques for when the anterior neck anatomy is not easily identifiable. The purpose of this video is to demonstrate these techniques for providers to review at regular intervals to enhance their preparedness and training. It may also serve as an education tool for instructors teaching the technique for the first time.</p> <p>This video was funded by a Rymer educational grant awarded at the University of Colorado Anschutz Medical Campus (Caterina Zagona-Prizio, MS III, project lead). We thank the donors and their families for their anatomical gifts, and the administrative staff of the Physical Therapy and Human Body Programs and the State of Colorado Anatomic Board. Video production by Bryan Less and Stephen Hall of CU Productions, University of Colorado Anschutz Medical Campus. Illustrations by Stefani Wilson.</p>
Agile Accelerator Program: From Industry-Academia Collaboration to Effective Agile Training
<p>The agile accelerator program takes place in a Brazilian technology park, as a collaboration between a university and a world-renowned technology company, specialized in agile development and consulting. This partnership has 8-year long with the main goal of preparing undergraduate students to work in high-performance agile teams. This partnership created a culturally rich environment for student learning while influencing other companies to follow the same initiative within this technology park. We conducted a Case Study aiming to characterize this partnership (explaining how it works) and the resulting program, understanding the benefits to the program students. Our results point out the importance of the kind of partnership that provides an immersive learning environment to students, where students can learn empirically, with real projects and real stakeholders and how important it was for the program's former students to enter the job market. This successful enhanced students' training program on agile software development through the blending of culture between institutions can be of inspiration to those interested in aiming to bridge the gap between academia and industry.</p>
Netflow data without sampling for training (D1)
<p>NetFlow traffic generated using <strong>DOROTHEA</strong> (<strong>DO</strong>cker-based f<strong>R</strong>amework f<strong>O</strong>r ga<strong>TH</strong>ering n<strong>E</strong>tflow tr<strong>A</strong>ffic)</p> <p>NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>NetFlow flows have been captured without sampling at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <p>In the construction of the datasets, different percentages of flows considered attacks and flows considered normal traffic have been used.</p> <p>These datasets have been used to train machine learning models.</p>
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
Training dataset: Mass spectrometry based proteomics of healthy human serum samples
<p>The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.</p>
Data management planning - Training for trainers, part I-III: answers to per-assignments
<p>The data have been collected as part of data management planning training for trainers. Data consists participants answers to pre-assignments.</p> <p>Consent for data sharing</p> <ul> <li>First session: Consent for datat sharing was asked afterwards by email</li> <li>Second and third session: Consent was asked when collecting answers on the e-form.</li> </ul> <p>The slides of the DMP training fro trainers is available on SlideShare:</p> <ul> <li>Session I: <a href="https://www2.slideshare.net/MariKuusniemi/part-i-data-management-planning-training-for-trainers">https://www2.slideshare.net/MariKuusniemi/part-i-data-management-planning-training-for-trainers</a></li> <li>Session II: <a href="https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-ii">https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-ii</a></li> <li>Session III: <a href="https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-iii">https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-iii</a></li> </ul> <p>The training was organised by Tuuli Office.</p>
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics (Training Dataset)
<p>This dataset consists of multiple files which contain bug prediction training data.</p> <p>The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).</p> <p>The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.</p> <p>We used different threshold values from which we considered the edges to be valid. The following threshold values were used:</p> <ul> <li>0.00</li> <li>0.05</li> <li>0.20</li> <li>0.30</li> </ul> <p>The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.</p> <p>There are four variants for all of these datasets:</p> <ul> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are: <ul> <li>ID</li> <li>Name</li> <li>Longname</li> <li>Parent ID</li> <li>Component ID</li> <li>Path</li> <li>Line</li> <li>Column</li> <li>EndLine</li> <li>EndColumn</li> </ul> </li> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)</li> <li>Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)</li> <li>Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)</li> </ul> <p>Static source code metrics which are contained in all dataset are the following:</p> <ul> <li>McCC - McCabe Cyclomatic Complexity</li> <li>NL - Nesting Level</li> <li>NLE - Nesting Level Else If</li> <li>CD - Comment Density</li> <li>CLOC - Comment Lines of Code</li> <li>DLOC - Documentation Lines of Code</li> <li>TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)</li> <li>TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)</li> <li>LLOC - Logical Lines of Code (Comment and empty lines not counted)</li> <li>LOC - Lines of Code (Comment and empty lines are counted)</li> <li>NOS - Number of Statements</li> <li>NUMPAR - Number of Parameters</li> <li>TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)</li> <li>TLOC - Lines of Code (Lines in embedded functions are also counted)</li> <li>TNOS - Total Number of Statements (Statements in embedded functions are also counted)</li> </ul>
Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data
<p>The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg):</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.</p>
NewsEye / READ OCR training dataset from French Newspapers (18th, 19th, early 20th C.)
<p>The dataset comprises French newspaper pages from 18th, 19th and early 20th century with carefully corrected text. The page images were provided by the <a href="https://www.bnf.fr/en">French National Library</a> and comprise 127 pages (training set) and 8 pages (validation set). The data are formed according to the PAGE format (cf. Cf. <a href="https://github.com/PRImA-Research-Lab/PAGE-XML/">https://github.com/PRImA-Research-Lab/PAGE-XML/</a>) and were produced with the <a href="http://read.transkribus.eu/">Transkribus </a>platform with support of the <a href="http://newseye.eu/">NewsEye</a> and the <a href="http://read.transkribus.eu/">READ </a>project.</p>
Training dataset: DIA data analysis of a HEK/Ecoli Spike-in dataset using OpenSwathWorkflow
<p>The eight raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured:</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.15 DIA<br> Sample2 2.5 0.15 DIA<br> Sample3 2.5 0.15 DIA<br> Sample4 2.5 0.15 DIA<br> Sample5 2.5 0.80 DIA<br> Sample6 2.5 0.80 DIA<br> Sample7 2.5 0.80 DIA<br> Sample8 2.5 0.80 DIA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured using data independent acquisition with a Q-Exactive Plus mass spectrometer. Briefly, a scan range from 400-1000 m/Z was first covered by an MS1 scan followed by 25 consecutive MS2 scans (each 24 m/z broad). In the next cycle another MS1 scan was acquired followd by 26 MS2 scans (also 24m/z broad) in which the window centers were shifted by 50% compared to the previous cycle of MS2 scans. The resulting raw files contain overlapping MS2 scans.</p> <p>Besides the eight raw files, we uploaded a spectral library, a transition list for the iRT peptides as well as an sample annotation file.<br> Additionally, we uploaded the Galaxy PyProphet score training result files: PyProphet score report and PyProphet score.</p>
Training dataset: Statistical analysis of a HEK/Ecoli Spike-in DIA dataset using MSstats
<p>The uploaded files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured and analysed using OpenSwathWorkflow in Galaxy. Results were exported using PyProphet and can be used for the statistical analysis and detection of the two different Spike-in Ratios. The Spike-in ratios were the following:</p> <p>Sample HEK E.coli <br> Spike_in_1 2.5 0.15<br> Spike_in_2 2.5 0.80 </p> <p>Besides the two PyProphet export files, we uploaded a sample annotation file as well as a comparison matrix file.<br> Additionally, we uploaded the Galaxy MSstats training result files: MSstats_ComparisonResult_export_tabular and MSstats_ComparisonResult_msstats_input.</p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
Training data for 'Genome annotation with Maker' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with Maker.</p> <p>It is based on data used in <a href="http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018">another Maker tutorial</a>.</p> <p>The full genome was <a href="https://www.ncbi.nlm.nih.gov/genome/?term=Schizosaccharomyces%20pombe[Organism]&cmd=DetailsSearch">downloaded from NCBI</a>, and mitochondria sequence removed from it for simplicity.</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.