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2,863 results for “Challenge”
IISWC 2021 Characterizing and Mitigating the I/O Scalability Challenges for Serverless Applications (Dataset and Scripts)
<p>As serverless computing paradigm becomes widespread, it is important to understand the I/O performance characteristics on serverless computing platforms. To the best of our knowledge, we provide the first study that analyzes the observed I/O performance characteristics -- some expected and some unexpected findings that reveal the hidden, complex interactions between the application I/O characteristics, the serverless computing platform, and the storage engines. The goal of this analysis is to provide data-driven guidelines to serverless programmers and system designers about the performance trade-offs and pitfalls of serverless I/O.</p>
2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)
<p>This dataset is part of the <a href="https://www.osgeo.org/foundation-news/2021-osgeo-un-committee-educational-challenge/?fbclid=IwAR0UvwkPO2pay7C0tJawb63eewjBGfeL9TIQpYUFccza9OIo6HAolmHXLWE">2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)</a>,</p> <p>Exercise 1: Supervised Change Detection: Monitoring deglaciation in Huascaran, Peru.</p>
Immune challenges increase network centrality in a queenless ant
<p>Social animals display a wide range of behavioural defences against infectious diseases, some of which inherently increase social contacts with infectious individuals (e.g., mutual grooming), while others decrease them (e.g., social exclusion). These defences often rely on the detection of infectious individuals, but this can be achieved in several ways that are difficult to differentiate. Here, we combine non-pathogenic immune challenges with automated tracking in colonies of the clonal raider ant to ask whether ants can detect the immune status of their social partners and to quantify their behavioural responses to this perceived infection risk. We first show that a key behavioural response elicited by live pathogens (allogrooming) can be qualitatively recapitulated by immune challenges alone. Automated scoring of interactions between all colony members reveals that this behavioural response increases the network centrality of immune-challenged individuals through a general increase in physical contacts. These results show that ants can detect the immune status of their nestmates and respond with a general "caring" strategy, rather than avoidance, towards social partners that are perceived to be infectious. Finally, we find no evidence that changes in cuticular hydrocarbon profiles drive these behavioural effects.</p>
Program Comprehension Challenges in Software Code Review
<p>Software engineers spend more time understanding code than writing it (with up to 70% of their time being devoted to this). A key activity where developers spend a lot of time reading and understanding code is software code review. Yet, little is known on the challenges concerning program comprehension during software code review. This study provides insight into the types of comprehension challenges that occur during code reviews and their causes. We find that missing design rationale is the most common reason for comprehension challenges in software code review. Comprehension challenges occur most commonly around five topics: “program logic,” “code design,” “defensive coding,” “condition checking,” and “concurrency”. We also show that machine learning (ML) can be used to automatically detect comprehension challenges with 74.3% precision and a 66.7% recall. We discuss potential improvements to code review support tools based on our findings.</p> <p>These files contain the trained ML algorithms and all the data collected and used for this study.</p> <p> </p> <p>Data was collected in 2018 with analysis performed in 2018/2019, but completion was delayed due to impacts of COVID19.</p>
RRS Challenger
One of NERC's research vessels employed during Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) cruises.
Future Blood Testing: Challenges & Opportunities
<p>This video shows the highlights of our Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 Music Licensing details: ASLC-1E1DFFF8-153AD9FEF0 The music is from Adobe Music, is called GUITAR-EPIC-TRAILER-VOCAL_AdobeStock_511629251</p> <p>YouTube Link: https://youtu.be/OfRa31Wvix8</p>
Document Liveness Challenge (DLC-2021) - part 3 (cc)
<p>Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information.</p> <p>Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.</p> <p><strong>Share and Cite</strong></p> <p><em>MDPI and ACS Style</em></p> <p>Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181</p> <p><em>AMA Style</em></p> <p>Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181</p> <p><em>Chicago/Turabian Style</em></p> <p>Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181</p>
Document Liveness Challenge (DLC-2021) - part 2 (re)
<p>Dataset DLC-2021 consists of 1424 video clips captured in a wide range of real-world conditions and focused on ID document forensics tasks. Each clip was shot vertically and was at least 5 seconds long. Frames extracted at 10 frames per second and for the 50 first extracted frames document position is manually annotated.<br> The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information.</p> <p>Part 1 contains videos, frames and markup for “original” laminated documents from MIDV-2020 collection and unlaminated gray copies. <br> Part 2 contains videos, frames and markup for documents recaptured from device screen<br> Part 3 contains videos, frames and markup for unlaminated color copies.</p> <p><strong>Share and Cite</strong></p> <p><em>MDPI and ACS Style</em></p> <p>Polevoy, D.V.; Sigareva, I.V.; Ershova, D.M.; Arlazarov, V.V.; Nikolaev, D.P.; Ming, Z.; Luqman, M.M.; Burie, J.-C. Document Liveness Challenge Dataset (DLC-2021). <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 181. https://doi.org/10.3390/jimaging8070181</p> <p><em>AMA Style</em></p> <p>Polevoy DV, Sigareva IV, Ershova DM, Arlazarov VV, Nikolaev DP, Ming Z, Luqman MM, Burie J-C. Document Liveness Challenge Dataset (DLC-2021). <em>Journal of Imaging</em>. 2022; 8(7):181. https://doi.org/10.3390/jimaging8070181</p> <p><em>Chicago/Turabian Style</em></p> <p>Polevoy, Dmitry V., Irina V. Sigareva, Daria M. Ershova, Vladimir V. Arlazarov, Dmitry P. Nikolaev, Zuheng Ming, Muhammad M. Luqman, and Jean-Christophe Burie. 2022. "Document Liveness Challenge Dataset (DLC-2021)" <em>Journal of Imaging</em> 8, no. 7: 181. https://doi.org/10.3390/jimaging8070181</p>
iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge
<p><a href="https://brainteaser.health/open-evaluation-challenges/idpp-2022/">iDPP@CLEF 2022</a> (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the <a href="https://brainteaser.health/">BRAINTEASER</a> Horizon 2020 project and co-located with <a href="https://clef2022.clef-initiative.eu/">CLEF 2022</a> (Conference and Labs of the Evaluation Forum). </p> <p>BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.</p> <p>The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:</p> <ul> <li> <p>Better describe<strong> disease mechanisms</strong>.</p> </li> <li> <p><strong>Stratify patients </strong>according to their phenotype assessed all over the disease evolution.</p> </li> <li> <p><strong>Predict disease progression</strong> in a probabilistic, time dependent fashion.</p> </li> </ul> <p>iDPP@CLEF 2022 offered the following tasks:</p> <ul> <li> <p><strong>Pilot Task 1 – Ranking Risk of Impairment</strong>: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain.</p> </li> <li> <p><strong>Pilot Task 2 – Predicting Time of Impairment</strong>: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window.</p> </li> <li> <p><strong>Position Papers Task 3 – Explainability of AI algorithms</strong>: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way.</p> </li> </ul> <p> </p> <p>This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems.</p> <p>For additional information about iDPP@CLEF 2022, please see:</p> <ul> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, <em>Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022)</em>, pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany.</p> </li> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, <em>CLEF 2022 Working Notes</em>, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. <a href="https://ceur-ws.org/Vol-3180/paper-88.pdf">http://ceur-ws.org/Vol-3180/</a>.</p> </li> </ul>
The PI-CAI Challenge: Zonal Segmentation for Prostate MRI (A. Karagöz et al., 2022)
<p>The zonal prostate segmentations have been generated by the algorithm from (A. Karagöz et al., 2022). See their <a href="https://rumc-gcorg-p-public.s3.amazonaws.com/evaluation-supplementary/642/88bd85e5-ca10-4979-875e-eba7476491d3/Hevi_picai_report.pdf">method description</a> for details. The segmentations indicate the peripheral zone as label 1, and the transition zone as label 2. Softmax predictions for these cases are stored as prostate_gland_sm_pz.nii.gz for the peripheral zone, and prostate_gland_sm_tz.nii.gz for the transition zone, in a folder with the subject id as the name.</p>
Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Non-systematic literature review of challenges that hinder the implementation of specific targets of the BDS 2030
<p>In the BioAgora project, Deliverable 1.1 focused on assessing and categorizing the challenges that can hinder the implementation of the EU Biodiversity Strategy 2030. A mixed-method research strategy was used to collect evidence on existing challenges, including a general and a targeted (non-systematic) literature review. This database summarizes the results of this non-systematic literature review as well as expert opinion shared on the initial results by project consortium members.</p> <p>The general literature search was carried out between the 1st September and the 10th October 2022, and contained the fixed keywords EU, European, biodiversity, conservation, strategy, implementation, evaluation as well as the interchangeable terms barrier, challenge, and issue. This search returned approximately 16,800 results of which the abstracts of the first 70 hits were screened for relevant guidance to establish the broad challenge categories. After the broad challenge categories were established, we launched a targeted literature search from the 10th October until the 5th November 2022. Here, a series of searches with the fixed terms EU, biodiversity, implementation, [target-specific input, e.g. invasive species, soil ecosystems, freshwater ecosystems] and the interchangeable terms challenge, barrier, and issue was conducted. Per search, the abstracts of the initial 15 results were screened and detailed information from the publications with relevance to the research question were extracted. Both searches included only publications from 2010 onwards and if possible, more recent publications were prioritised over older ones.</p>
Data for manuscript, "An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples"
<p>Clinical BALF samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, MS based proteomic analysis of BALF is impeded by the dynamic range of protein abundance, and potential for interfering contaminants. We have developed a workflow that eliminates these challenges. By combining high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, our workflow is compatible with both qualitative and quantitative MS-based proteomic analysis. The workflow includes collection of endogenous peptides for peptidomic analysis of BALF, if desired, as well as amenability to offline semi-preparative or microscale fractionation of peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We show the effectiveness of this workflow on BALF samples from COPD patients. Overall, our workflow should allow MS-based proteomics to be applied to a wide variety of studies focused on BALF clinical samples. </p> <p>Note: Due to the nature of some of the files, file <em>wendt005_ostr0103_18260_20210831_BALF_FAIMS_MS2_TMT16.msf, wendt005_ostr0103_18976_20230202_quantReport.msf, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_1R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_2R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_3R.raw and cmsptc_higgi022_18988_20230203_18976DW_Eclipse_noFAIMS_quantReport.msf</em> were zipped into compressed folders before uploading.</p>
Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis
<p>The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in<br> simulations of electrocatalysis".</p> <p>Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.</p>
Age, but not an immune challenge, triggers terminal investment in the Pacific field cricket
<p>The terminal investment hypothesis proposes that, when individuals are faced with a threat to survival, they will increase investment in current reproduction. The level of the threat necessary to elicit terminal investment (the dynamic terminal investment threshold) may vary based on other factors that also influence future reproduction. Here, we tested whether there is an interactive effect of age and an immune challenge on the dynamic terminal investment threshold in the Pacific field cricket, <em>Teleogryllus</em> <em>oceanicus</em>. We measured the courtship call, mating attractiveness, ejaculate size and offspring production of <em>T. oceanicus</em> males. We found only limited support for the dynamic terminal investment threshold: there was no consistent evidence of a positive interaction between male age and immune challenge intensity. However, we found evidence for age-related terminal investment: older males produced a larger spermatophore than younger males. Older males also had a slower calling rate compared to younger males, suggesting a potential trade-off between these two pre- and post-copulatory traits. As some, but not, all reproductive traits responded plastically to cues for terminal investment, our research highlights the importance of considering a broad range of pre-and post-copulatory traits when exploring the potential for terminal investment to occur.</p>
Challenges simulating the AMOC in climate models
<p>This is a data set for the article:</p> <p>Jackson, Laura C, Helene T. Hewitt, Diego Bruciaferri, Daley Calvert, Tim Graham, Catherine Guiavarc’h, Matthew B. Menary, Adrian L. New, Malcolm Roberts and David Storkey, 2023: Challenges simulating the AMOC in climate models, submitted to Philosophical Transactions of the Royal Society A.</p> <p>Fig 1 calculated correlations between AMOC strength or trend in 1% CO2 experiment (values given in CMIP6_AMOC.txt) and sea surface salinity fields in the controls (fields for each model given in CMIP6_SSS.nc)</p> <p>We also include data of the AMOC streamfunction (*amoc.nc), March mixed layer depth (MarchMLD.nc) and temperature and salinity fields (TS.nc) for the mean of years 20-30 of models HadGEM3-GC3-1ML, MM and MH. These models are documented here <a href="https://doi.org/10.5194%2fgmd-2019-148">doi:10.5194/gmd-2019-148</a>. This data was used for plotting figures 2-4. </p> <p>Data for Fig 5 is in HadGEM3-GC3-1MM-noGM-av-20-40_MarchMLD.nc and HadGEM3-GC3-1MM-withGM-av-20-40_MarchMLD.nc</p> <p>Data for Fig 6 is in HadGEM3-GC1_control_20390301_20780330_MLD.nc and HadGEM3-GC1_relax_20390301_20780330_MLD.nc</p> <p>Data for Fig 7 is included in <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F7764695&data=05%7C01%7Claura.jackson%40metoffice.gov.uk%7Cceeee8cf4c9d4401060e08db2bcc93f2%7C17f1816120d7474687fd50fe3e3b6619%7C0%7C0%7C638151929867577211%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nkL88P4lDCDB5E4qPxvL6%2B6pu6nU5kDtCofZg5%2FAoDs%3D&reserved=0">https://zenodo.org/record/7764695</a> </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.