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Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Curlie Enhanced with LLM Annotations: Two Datasets for Advancing Homepage2Vec's Multilingual Website Classification
<h3>Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification</h3> <p>This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.</p> <p><strong>Key Features:</strong></p> <ul> <li><strong>LLM-generated annotations:</strong> Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.</li> <li><strong>Improved multi-label classification:</strong> Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.</li> <li><strong>Multilingual applicability:</strong> The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.</li> </ul> <p><strong>Dataset Composition:</strong></p> <ul> <li><strong>curlie-gpt3.5-10k:</strong> 10,000 websites labeled using GPT-3.5, context 2 and 1-shot</li> <li><strong>curlie-gpt4-10k:</strong> 10,000 websites labeled using GPT-4, context 2 and zero-shot</li> </ul> <p><strong>Intended Use:</strong></p> <ul> <li>Fine-tuning and advancing Homepage2Vec or similar website classification models</li> <li>Research on LLM-generated datasets for text classification tasks</li> <li>Exploration of multilingual website classification</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li><strong>Project and report repository:</strong> https://github.com/CS-433/ml-project-2-mlp</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with <a href="https://people.epfl.ch/robert.west">Prof. Robert West</a> and <a href="https://tizianopiccardi.github.io/" rel="nofollow">Tiziano Piccardi.</a></p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
X-Shooting ULLYSES: Massive Stars at low metallicity - II. DR1: Advanced optical data products for the Magellanic Clouds
<p>Xshooter optical spectroscopic data of Magellanic Clouds targets observed by the ESO Large Program X-Shooting ULLYSES: Massive Stars at low metallicity (PI: Vink; Porgram ID: 106.2011Z). <br><br>This version is identical to the previous version but includes the LMC and SMC atlases and the static calibration files with the new flux models (all arms) and spline anchor points (only UVB).</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"
<p>This is a file collection as supplementary material for the paper <em>Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>.</em> It consists of:</p> <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> <p>The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in <em>Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a></em>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.</p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Solar Wind properties measured with instruments on the Advanced Composition Explorer (ACE)
<p>Combined ACE/SWEPAM, ACE/Mag, and ACE/SWICS data set<br> ACE/MAG and ACE/SWEPAM data are taken from the ACE Science center (https://izw1.caltech.edu/ACE/ASC/) and binned to the 12-minute time resolution of SWICS.<br> The SWICS data is based on the PHA data and analyzed as described in Berger (2008).<br> This data set is used in the following two publications:<br> Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023, submitted), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>Contact: Verena Heidrich-Meisner, CAU Kiel heidrich@physik.uni-kiel.de</p> <p>We thank the science teams of ACE/SWEPAM, ACE/MAG as well as<br> ACE/SWICS for developing, maintaining and calibrating the instruments and for providing the respective level 2 and level 1 data products.<br> This work was supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) as SOHO/CELIAS 50 OC 2104.</p> <p>Data products description:<br> year: year of observation (int)<br> time: day of year in current year as float<br> yeartime: time in years as float (UTC)<br> vsw: solar wind proton speed in km/s, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> dsw: solar wind proton density in cm^{-3}, measured by ACE/SWEPAM (level 2 from ACE Science Center)and rebinned to 12 minute time resolution<br> tsw: solar wind proton temperature in K, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> B: magnetic feld strength in nT, measured by ACE/MAG (level 2 from ACE Science Center)<br> colage: proton-proton collisional age computed as 6.4* 1e8 * dsw /(vsw* tsw**(3/2)) in K^{3/2} s^2 cm^3 km^{-1}<br> dO7_6: ratio of the O7+ to O6+ charge state densities, measured by ACE/SWICS, derived directly from PHA (pulse height analysis) data<br> eO7_6: estimate of the relative error of dO7_6 based on the counting statistics<br> ldO7_6: decadic logarithm of dO7_6<br> elO7_6: estimate of the relative error of the decadic logarithm dO7_6 based on the counting statistics<br> mcsFe: mean charge state of Fe, based on SWICS PHA of Fe8+, Fe9+, Fe10+, Fe11+, and Fe12+ in units of the elementary charge e. At least 10 counts distributed over Fe8+, Fe9+, Fe10+, Fe11+ and Fe12+ are required<br> emcsFe: estimate of the relative error of dO7_the mean Fe charge state in e (assumes 10% relative error for each Fe charge state)<br> cor_hole: coronal hole wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> sec_rev: sector reversal plasma wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> stream_belt: streamer belt wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> ICME: interplanatery coronal mass ejections time periods (with a six hour safety margin before and after each ICME) from the Jian (2006,2011) and Richardson & Cane (2014, 2018) ICME lists. Entries are 0 or 1, 1 of the data point is assigned to this type.<br> totalCountsFe: number of counts in ACE/SWICS distributed over Fe8+-Fe12+<br> The data set is restricted to data points where valid data points are available for all listed data products. Only for the mean charge state of Fe invalid data points are indicated with nan (not a number)</p> <p>References:<br> Berger, L. 2008, PhD thesis, Kiel, Christian-Albrechts-Universität, Diss., 2008<br> Gloeckler, G., Cain, J., Ipavich, F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 497–539<br> McComas, D., Bame, S., Barker, P., et al. 1998b, in The Advanced Composition Explorer Mission (Springer), 563–612<br> Smith, C. W., L’Heureux, J., Ness, N. F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 613–632</p> <p>Xu, F. & Borovsky, J. E. 2015, Journal of Geophysical Research: Space Physics, 120, 70<br> Heidrich-Meisner, V., Berger, L., & Wimmer-Schweingruber, R. F. 2020, Astronomy & Astrophysics, 636, A103<br> Jian, L., Russell, C., & Luhmann, J. 2011, Solar Physics, 274, 321<br> Jian, L., Russell, C., Luhmann, J., & Skoug, R. 2006, Solar Physics, 239, 393<br> Richardson, I. G. 2004, Space Science Reviews, 111, 267<br> Richardson, I. G. 2018, Living reviews in solar physics, 15, 1</p> <p>Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>year/1 time/day of year yeartime/UTC vsw/km/s dsw/cm^{-3} tsw/K B/nT colage/(K^{3/2} s^2 cm^3 km^{-1}) dO7_6/1 eO7_6/1 ldO7_6/1 elO7_6/1 mcsFe/e emcsFe/e cor_hole/bool sec_rev/bool stream_belt/bool ICME/bool totalCountsFe/1</p>
Raw Data of the Surveys conducted in the Advanced Inorganic Lab Course in the Winter Terms 2020/2021, 2021/2022, and 2022/2023 at RWTH Aachen University
<p>In the advanced inorganic lab course at RWTH Aachen University, the undergraduate students are asked to use the electronic laboratory notebook (ELN) Chemotion and thus become aware of and familiar with research data management (RDM) at an early stage in their studies. To map the implementation of research data management and the Chemotion ELN in the lab course, a survey was conducted in the winter terms 2020/2021, 2021/2022, and 2022/2023 to ask the students to share their experiences and criticism on these topics.</p> <p>In this data publication, the underlying raw data of the surveys (as received from the survey software SoSci Survey<sup>1</sup>) are available as .csv-files separated into data, values, and variables for the respective winter terms. Additionally, the evaluated data are summarized in .xlsx-files which are also part of this data publication. As the principal language of the inorganic lab course is German, the survey and it's evaluation are primarily in German language, too. For further information, please have a look at the 01_Read-me.txt file.</p> <p>The survey and the related results and interpretations are available as a journal publication elsewhere.</p> <p><strong>Literature:</strong></p> <p>1. Leiner, D. J. <em>SoSci Survey (Version 3.2.12 and newer) [Computer software]</em>. 2020.<strong> </strong><a href="https://www.soscisurvey.de">https://www.soscisurvey.de</a><em> </em>(accessed 2023-07-26).</p> <p> </p>
Advanced Resolution Canopy FLOw (ARCFLO) experiment employing the SUbcanopy Sonic Anemometer Network (SUSAN) in WS01 of the HJ Andrews Experimental Forest, July-September 2012
This dataset was collected during one of the ARCFLO (Advanced Resolution Canopy FLOw) experiment series’ field campaigns. This field campaign was carried out in WS1 of the HJ Andrews Experimental forest during July-September 2012 by the biomicrometeorology group, PI Christoph Thomas. The ARCFLO experiment series spanned a wide range of topographic conditions (flat, sloped, mountainous) and canopy architectures (grassland, orchard, open forest, dense forest) and was carried out between 2011 and 2014. It was funded through the NSF Career Award in Physical and Dynamical Meteorology to PI Christoph Thomas. The main goal of this project was to develop a novel improved framework to describe the airflow and its transport under weak-wind conditions for a continuous variation of overstory density and stratification. The objective is to i) identify forcing mechanisms of submeso motions, ii) evaluate the impact of plant canopies of different overstory density on the wind, temperature, and humidity fields, and iii) improve predictors for mixing in plant canopies that incorporate the important physical mechanisms. Observations were be made with a unique combination of new and standard techniques including optical fiber measurement of temperature structure, acoustic remote sensing, ultrasonic anemometers, and laser-illuminated flow visualizations.
MCR LTER: Coral Reef: Biodiversity has a positive but saturating effect on imperiled coral reefs; data for Clements and Hay 2021, Science Advances
Species loss threatens ecosystems worldwide, but the ecological processes and thresholds that underpin positive biodiversity effects among critically important foundation species, such as corals on tropical reefs, remain inadequately understood. In field experiments, we manipulated coral species richness and intraspecific density to test whether, and how, biodiversity affects coral productivity and survival. Corals performed better in mixed species assemblages. Improved performance was unexplained by competition theory alone, suggesting that positive effects exceeded agonistic interactions during our experiments. Peak coral performance occurred at intermediate species richness and declined thereafter. Positive effects of coral diversity suggest that species’ losses on degraded reefs make recovery more difficult and further decline more likely. Harnessing these positive interactions may improve ecosystem conservation and restoration in a changing ocean. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site. Datasets used in this study are available online from the BCO-DMO data system. Data for this paper can be found at (https://www.bco-dmo.org/project/837802).
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </p>
Data from: Advancement in long-distance bird migration through individual plasticity in departure
<p>Research summary: Globally, bird migration is occurring earlier, consistent with climate-related changes in breeding resources. Although often attributed to phenotypic plasticity, there is no clear demonstration of long-term population advancement in avian migration through individual plasticity. Using direct observations of bar-tailed godwits (<em>Limosa lapponica</em>) departing New Zealand on a 16,000-km journey to Alaska, we show that migration advanced by six days during 2008–2020, and that within-individual advancement was sufficient to explain this population-level change. However, in individuals tracked for the entire migration, earlier departure did not lead to earlier arrival or breeding in Alaska, due to prolonged stopovers in Asia. Moreover, changes in breeding-site phenology varied across Alaska, but were not reflected in within-population differences in advancement of migratory departure. We demonstrate that plastic responses can drive population-level changes in timing of long-distance migration, but also that behavioral and environmental constraints <em>en route</em> may yet limit adaptive responses to global change.</p> <p>The collection of long-term departure data was supported by Chris & Neville Hopkins, David & Lucile Packard Foundation, Dobberke Foundation for Comparative Psychology, Manawatu Estuary Trust, Marsden Fund (Royal Society of New Zealand), Massey University Doctoral Scholarship, New Zealand Department of Conservation, Ornithological Society of New Zealand, Pacific Shorebird Migration Project, Pūkorokoro Miranda Naturalist’s Trust, and Royal Netherlands Academy of Arts & Sciences.</p>
Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)
<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br> In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </p>
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
MelanoDB: Dataset files of clinical and molecular features of advanced melanoma patients treated with MAPK inhibitors
<p>MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles</p> <p>These data are available under the licence CC-BY-SA.</p> <p>Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/</p> <p>You are requested to cite this repository in the case of using these data in a publication.</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.