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Coupled Changes Case Study Analysis
<p>Are Suggestions about Coupled Changes<br /> Interesting? – Software Repository Mining Case Study</p> <ul> <li>Survey questions</li> <li>Interview questions</li> <li>Grounded theory open coding</li> <li>Analysis results</li> <li>questionnaire results</li> </ul>
Lulu - a software simulator for P colonies. Use case scenarios and demonstration videos
<p>The videos show three different examples of using the Lulu P colony simulator.</p> <p>The Lulu P colony simulator is available under an open-source MIT license at https://github.com/andrei91ro/lulu_pcol_sim. All of the secondary applications, including Lulu_Kilobot are available (also under open-source licenses) at https://github.com/andrei91ro.</p> <p>The first two videos present the simulator running addition (+1) and subtraction (-1). In these two examples, the simulator is ran in a step by step mode in order to clearly visualize the results of running each simulation step. For this reason the total simulation time reported at the end of the simulation is in the order of minutes.</p> <p>The average (of five runs) simulation time for a normal (non-interactive) simulation is 0.0021050 seconds for the addition and 0.0047492 seconds for the subtraction examples.</p> <p>The third example (lulu_kilobot_30_steps) presents the simulator running a more complex P colony that controls a Kilobot robot simulated in V-REP. This P colony is based on the subtraction P colony in the sense that each move the robot makes is marked by the removal of an f object from the environment.</p> <p>The input file used in the addition example (lulu_sim_ag_increment):</p> <p>pi = {<br /> A = {l_p};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_p};<br /> B = {AG_1};<br /> AG_1 = ({e, e}; < e->f, e<->l_p >, < l_p->e, f<->e >);<br /> }</p> <p>The input file used in the subtraction example (lulu_sim_ag_decrement):</p> <p>pi = {<br /> A = {l_m, l_p, l_z};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_m};<br /> B = {AG_1};<br /> AG_1 = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->l_p, e<->f/e<->e >,<br /> < f->e, l_p<->e >,<br /> < l_p->l_z, e<->e >,<br /> < e->e, l_z<->e > );<br /> }</p> <p>The input file used in the Kilobot example (lulu_kilobot_30_steps):</p> <p>pi = {<br /> A = {l_m, m_0, m_S, m_L, m_R, c_R, c_G, c_B};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, l_m};<br /> B = {AG_command, AG_motion};<br /> AG_command = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->m_S, e<->f/e<->e >,<br /> < f->e, m_S<->e >,<br /> < m_S->m_0, e<->e >,<br /> < e->e, m_0<->e > );</p> <p> AG_motion = ({e, e};<br /> < e->l_m, e<->m_S >,<br /> < m_S->e, l_m<->e/e->e ><br /> < e->e, e<->m_0 >,<br /> < m_0->e, e<->m_0/e->e >);<br /> }</p> <p> </p>
Nivolumab-associated acute glomerulonephritis: Case report and literature review
<p><strong>Background:</strong> Immune checkpoint inhibitors are changing the landscape of oncologic treatment as they are significantly improving treatment of multiple malignancies. Nivolumab, anti PD-1 antibody, is FDA-approved for treatment of melanoma, non-small cell lung cancer and kidney cancer and can cause a spectrum of autoimmune reaction. The adverse effect can occur at any organ system in the body, but most commonly affects colon, lung, liver or endocrine system. Although rare, kidneys also can be involved.</p> <p><strong>Case presentation: </strong>A 70 year old male with clear cell kidney cancer was admitted with acute kidney injury while he was receiving nivolumab treatment. A kidney biopsy revealed diffuse tubular injury and immune complex mediated glomerulonephritis. Electron microscopic exam of the specimen demonstrated hump-like subepithelial deposits. Nivolumab was discontinued and the patient was started on high dose steroid. After 5 months of systemic corticosteroid and hemodialysis, patient’s kidney function improved to his baseline. In spite of the prolonged treatment interruption, immunosuppressive therapy did not compromise the anticancer effect of nivolumab.</p> <p><strong>Conclusion: </strong>Immune-related adverse effect in kidney can cause autoimmune glomerulonephritis as well as tubulo-interstitial injury. In previous literature, immune-related nephritis generally responded well to systemic corticosteroid. Based our experience, prolonged course of high dose of steroid and hemodialysis may be required to achieve adequate treatment effect. </p>
Microblogging violent attacks on medical staff: A case study of the Longmen County People’s Hospital incident, an indication of a health care crisis in China
<p>A violent attack on medical staff in Guangdong Province, in which a female doctor at Longmen County People’s Hospital (LCPH) was severely injured by a knife-wielding patient, has drawn significant public attention to the phenomenon of hospital violence and initiated discussions on how to resolve violence in hospitals. Social networking sites, such as Sina Weibo, a Chinese version of Twitter, have played a role in this public debate. The incident at LCPH provides an opportunity to examine how Weibo has been used in the debate about violence against medical staff in China. Using the Sina Weibo’s built-in search tool, we established a dataset of 661 Chinese-language micro-blogs containing the search terms: Longmen (“龙门”), doctor (“医生”), and slash (“砍”) that were posted between July 15, 2015, the date of the violent incident at LCPH, and August 15, 2015. We performed a content analysis of the micro-blogs to examine: users’ demographics, attitudes toward the injured doctor and the attacker, possible reasons for the hospital violence, and proposed measures for preventing doctors from violent incidents. </p>
SBML Test Suite Syntactic Test Cases 3.2.0
<p>The SBML Test Suite is a conformance testing system. It allows developers and users to test the degree and correctness of the SBML support provided in a software package. A core part of the SBML Test Suite is the collection of test cases. There are 3 sets of tests: <strong>semantic</strong> (for deterministic simulation behavior), <strong>stochastic</strong>(for stochastic simulation behavior), and <strong>syntactic</strong> (for basic parsing).</p> <p>This is the version 3.2.0 release of the s<strong>yntactic</strong> test cases archive.</p> <p>For more information about SBML and the SBML Test Suite, please visit http://sbml.org.</p>
SBML Test Suite Semantic Test Cases 3.2.0
<p>The SBML Test Suite is a conformance testing system. It allows developers and users to test the degree and correctness of the SBML support provided in a software package. A core part of the SBML Test Suite is the collection of test cases. There are 3 sets of tests: <strong>semantic</strong> (for deterministic simulation behavior), <strong>stochastic</strong>(for stochastic simulation behavior), and <strong>syntactic</strong> (for basic parsing).</p> <p>This is the version 3.2.0 release of the <strong>semantic</strong> test cases archive.</p> <p>For more information about SBML and the SBML Test Suite, please visit http://sbml.org.</p>
SBML Test Suite Stochastic Test Cases 3.2.0
<p>The SBML Test Suite is a conformance testing system. It allows developers and users to test the degree and correctness of the SBML support provided in a software package. A core part of the SBML Test Suite is the collection of test cases. There are 3 sets of tests: <strong>semantic</strong> (for deterministic simulation behavior), <strong>stochastic</strong>(for stochastic simulation behavior), and <strong>syntactic</strong> (for basic parsing).</p> <p>This is the version 3.2.0 release of the <strong>stochastic</strong> test cases archive.</p> <p>For more information about SBML and the SBML Test Suite, please visit http://sbml.org.</p>
Fig. 1 in A case of disjunct montane linyphiid species (Araneae) in the Palaeotropics, with notes on synonymy and the description of a new species
Fig. 1. Micrargus fuscipalpis (Denis, 1962) comb. nov., female holotype. Abdomen, ventral view.
Software Evolution and Quality Data from Controlled, Multiple, Industrial Case Studies
<p>This data was obtained from a controlled, multiple case study involving six professional developers and four real-life, industrial systems. The study was designed to control for the moderator factors: programmer skill, maintenance task and learning effect. The primary data set contains multiple sets of defects, in the form of reports (excel files) extracted from six issue tracking systems. The secondary data consists of a series of attributes extracted from the software systems (i.e., code smells) and their evolution (i.e., code churn), and a log specifying the dates on which developers worked on each of the systems/tasks, in the form of excel files. Details on the controlled, multiple case study can be found in the doctoral dissertation by Yamashita titled: "Assessing the Capability of Code Smells to Support Software Maintainability Assessments: Empirical Inquiry and Methodological Approach" (online) Available at: https://www.duo.uio.no/handle/10852/34525</p>
High resolution images for 'Identification of "BRAF-positive" cases based on whole-slide image analysis'
<p>This archive contains high resolution images to accompany the article 'Identification of “BRAF-positive” cases based on whole-slide image analysis' by V. Popovici, A. Krenek and E. Budinska.</p>
Data and trained word2vec model for ``Easy over Hard: A Case Study on Deep Learning''
<p>The data include: training and testing data pairs</p> <p>The word2vec model is pre-trained. </p> <p>More details, please refer to the paper</p>
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
<p>This dataset contains the models for interpretable Word Sense Disambiguation (WSD) that were employed in Panchenko et al. (2017; the paper can be accessed at https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/EACL_Interpretability___FINAL__1_.pdf).</p> <p>The files were computed on a 2015 dump from the English Wikipedia. Their contents:</p> <ul> <li>Induced Sense Inventories: <strong>wp_stanford_sense_inventories.tar.gz</strong><br> This file contains 3 inventories (coarse, medium fine)</li> <li>Language Model (3-gram): <strong>wiki_text.3.arpa.gz</strong><br> This file contains all n-grams up to n=3 and can be loaded into an index</li> <li>Weighted Dependency Features: <strong>wp_stanford_lemma_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000.gz</strong><br> This file contains weighted word--context-feature combinations and includes their count and an LMI significance score</li> <li>Distributional Thesaurus (DT) of Dependency Features: <strong>wp_stanford_lemma_BIM_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000_simsortlimit200_feature expansion.gz</strong><br> This file contains a DT of context features. The context feature similarities can be used for context expansion</li> </ul> <p>For further information, consult the paper and the companion page: http://jobimtext.org/wsd/</p> <p>Panchenko A., Ruppert E., Faralli S., Ponzetto S. P., and Biemann C. (2017): Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL'2017). Valencia, Spain. Association for Computational Linguistics.</p> <p> </p> <p> </p>
10 eADAGE models for the PAO1 KEGG pathways case study in PathCORE
<p>ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data.</p> <p>eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659).</p> <p>The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh). </p>
P. aeruginosa expression compendium for the PAO1 KEGG pathways case study in PathCORE.
<p>The normalized P. aeruginosa (PAO1) gene expression compendium uses datasets from ArrayExpress. Each gene expression vector is linearly normalized to the range between 0 and 1.</p> <p>The script used to download the PAO1 datasets and process all samples into a single expression compendium is available here: https://bitbucket.org/greenelab/eadage/src/d8a98586e0f448a5b7d497f5516cc4e241635361/data_collection/data_collection.sh</p>
Data and codes for Äspö field case
<p>These codes and data sets were used to create the joint inversion results which are published in:</p> <p>Ronczka M., Hellmann K., Günther T., Wisen R. and Dahlin T.;"Electric resistivity and seismic refraction tomography, a challenging joint underwater survey at Äspö Hard Rock Laboratory"; Solid Earth, 8, 671-682, 2017; doi: 10.5194/se-8-671-2017</p>
Pre-trained word2vec models for ``Easy over Hard: A Case Study on Deep Learning''
<p>Since the whole stack overflow dump is so big, we can't easily handle well. Here, we provide 10 pre trained word2vec models with different seeds.</p> <p> </p> <p>More details about how to use it, please see paper </p>
Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean
<p><span><span>Salinity variations in Arctic Ocean determine the strength of stratification,</span> <span>ocean circulation, and biogeochemical cycles. Therefore, a<span>ccurate </span>salinity product is of great significance for our study of the Arctic Ocean. The mean density structure and wind-driven surface circulation of the Arctic Ocean are largely dominated by the anti-cyclonic Beaufort Gyre in the Canadian Basin, along with the Transpolar Drift<span> (Hall </span><span>et al.,2022)</span>. We focus on the salinity in Western Arctic Ocean. Multiple machine learning methods were used to reconstruct annual salinity product in the Western Arctic Ocean temporal for the period 2003-2022.</span></span></p>
Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires
<p>This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (R2=0.878), surpassing existing methods in spatial resolution and performance. </p> <p>Inside the Replication Package folder, the user can replicate the main results from the paper. This includes:</p> <ol> <li> <p><strong>Small Area Estimation (SAE) Replication:</strong></p> <ul> <li> <p><strong>Argentina Household Survey Data (EPH):</strong> Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta).</p> </li> <li> <p><strong>Argentina Census Microdata:</strong> Raw 2010 census microdata (censo2010_fullraw_p.dta).</p> </li> <li> <p><strong>Census Tract Map:</strong> Shapefile of 2010 census tracts (radios_eph_with_link.shp).</p> </li> <li> <p><strong>SAE Output:</strong> The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model.</p> </li> </ul> </li> <li> <p><strong>CNN-based Income Prediction Replication (Paper Results):</strong></p> <ul> <li> <p><strong>CNN Model Income Predictions:</strong> Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp).</p> </li> <li> <p><strong>Normalization Scalars:</strong> A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos).</p> </li> <li> <p><strong>World Settlement Footprint (WSF):</strong> Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas.</p> </li> </ul> </li> </ol> <p>Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif.</p> <p>Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below.</p> <p>For researchers who wish to replicate the full analysis pipeline starting from the original source imagery, the data must be acquired commercially. The proprietary Pleiades and Pleiades NEO satellite imagery is owned by Airbus and can be purchased through their data portal: https://space-solutions.airbus.com/imagery/. To facilitate this process, we provide the unique product identifiers for each scene used in this study. These identifiers can be used to query the Airbus archive and purchase the exact scenes.</p> <ul> <li><strong>Pléiades</strong>: for 2013 imagery the IDs are DS_PHR1A_201302051411520_FR1_PX_W059S35_0807_03124, DS_PHR1A_201302071357305_FR1_PX_W059S35_0410_06105 and DS_PHR1A_201302071357509_FR1_PX_W059S35_0609_05426, and for 2018, DS_PHR1A_201803251356358_FR1_PX_W059S35_0909_03875, DS_PHR1A_201808021356574_FR1_PX_W059S35_0509_06938 and DS_PHR1A_201808021357186_FR1_PX_W059S35_0706_06104.</li> <li><strong>Pleiades NEO</strong>: for 2022 imagery the IDs used are 000047717_1_22_STD_A, 000047717_1_24_STD_A, 000047717_1_25_STD_A, 000047717_1_26_STD_A, 000058605_1_3_STD_A, 000058605_1_4_STD_A, 000058605_1_7_STD_A, and 000058608_1_2_STD_A.</li> </ul> <p><strong>Important Usage Note:</strong> Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision.</p> <p>Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.</p> <p>Data can be visualized by accessing: <a href="https://ingresoamba.netlify.app">https://ingresoamba.netlify.app</a></p> <p> </p> <p> </p>
Constraints for Rotor Inflow Benchmarks - Unstable Cases
<p>Data to be used as constraints for the rotor inflow benchmarks (unstable cases) as defined in https://rotor-inflow-benchmark.readthedocs.io/</p> <p> </p> <p>GL*.csv : time-averaged wind speed and veer over each period, obtained from ground-based vertically profiling lidar</p> <p>UVW_real*.csv : high-frequency (20 Hz) time series of three-dimensional wind velocity vector over 24 hours centered on the periods of interest, obtained from meteorological tower sonic anemometers</p> <p>NL*.nc: low-frequency (1 Hz) time series of longitudinal wind velocity over each period, obtained from nacelle-mounted scanning lidar after quality control, temporal upsampling, interpolation to a regular grid and the use of a machine learning model to fill in gaps introduced by blade passage in front of the lidar</p> <p>LES*.nc: low-frequency (1 Hz) time series of longitudinal wind velocity over each period, obtained from large-eddy simulations performed to match the experimental field measurements</p> <p>UVW_les*.csv: high-frequency (16 Hz) time series of three-dimensional wind velocity vector at two heights for the 704 seconds of the large-eddy simulations</p>
Data for: When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation
<p>This is the data repository for our study on “When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation” submitted to the Journal of Industrial Ecology (JIE).</p> <p>I contains:</p> <ul> <li>The data behind the all numeric plots</li> <li>The data needed to reproduce the case-study results in the Supplementary Information (check out V1)</li> </ul> <p> </p> <p>To reproduce our results you need to download the files from this repo, our code from Github (https://github.com/simschul/uncertainty_disaggregation) and put the data into the `./data` folder. </p> <p>The data is an intermediate output from an earlier study: https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html </p> <p>For more information how those intermediate results were created please refer to the paper and the code (https://github.com/simschul/uncertainty_GHG_accounts)</p> <p> </p> <p> </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.