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2,863 results for “Challenge”
Data set Procedural Challenges
<p>For the article "Procedural Challenges: A case study in sharing PRC electronic resources" (submitted to the International Journal of Digital Humanities", special issue "Digital Humanities in East Asian Studies", editors Hilde De Weerdt and Aliz Horvath) we conduct a literature review of research into Republican Chinese newspapers as it appears in popular Chinese academic journal databases and their accompanying data publications. In essence, we repeatedly run identical keyword searches using different interfaces and compare results. We first gather materials in the form of saved html files and screenshots which are then prepared for further analysis.</p> <p>The data set contains the source files that support the findings of our study, along with descriptive metadata and additional sources not used in the final article version. All data is collected on Windows 10 Enterprise Edition, using Firefox Browser 88.0.1, and cross-checked with both Google Chrome 90.0.4430.212 and Microsoft Edge 90.0.818.62.</p>
GENEA Challenge 2022 3D coordinates of submitted motion
<p>This Zenodo repository contains Cartesian 3D coordinates in npy format for all test-set motion submitted by teams participating in the GENEA Challenge 2022. The npy format is easily readable using the NumPy package in Python.</p> <p> </p> <p>Attribution:</p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-04) this is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p><a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p> </p> <p>Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>*<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/"> https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>*<a href="https://genea-workshop.github.io/2022/challenge/"> https://genea-workshop.github.io/2022/challenge/</a></p>
GENEA Challenge 2022 3D coordinates of submitted motion
<p>This Zenodo repository contains Cartesian 3D coordinates in npy format for all test-set motion submitted by teams participating in the GENEA Challenge 2022. The npy format is easily readable using the NumPy package in Python.</p> <p> </p> <p>Attribution:</p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-04) this is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p><a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p> </p> <p>Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>*<a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/"> https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>*<a href="https://genea-workshop.github.io/2022/challenge/"> https://genea-workshop.github.io/2022/challenge/</a></p>
Data from: Microglia are necessary to regulate sleep after an immune challenge
<p>Microglia play a critical role in the neuroimmune response, but little is known about the role of microglia in sleep following an inflammatory trigger. Nevertheless, decades of research have been predicated on the assumption that an inflammatory trigger increases sleep through microglial activation. We hypothesized that mice (n = 30) with depleted microglia would sleep less following administration of lipopolysaccharide (LPS) to induce inflammation. Brains were collected and microglial morphology was assessed using quantitative skeletal analyses, and physiological parameters were recorded using non-invasive piezoelectric cages. Mice fed PLX5622 (PLX) diet for 3 weeks had a transient increase in sleep that dissipated by week 2. Subsequently, following a first LPS injection (0.4 mg/kg), mice with depleted microglia slept more than mice on control diet. All mice were returned to normal rodent chow to repopulate microglia in the PLX group (10 days). Nominal differences in sleep existed during the microglia repopulation period. However, following a second LPS injection, mice with repopulated microglia slept similarly to control mice during the dark period but with longer bouts during the light period. Comparing sleep after the first LPS injection to sleep after the second LPS injection, controls exhibited temporal changes in sleep patterns with no change in cumulative minutes slept, whereas in mice with repopulated microglia cumulative sleep decreased during the dark period across all days. Microglia repopulated after PLX also had a reactive morphology with fewer branch endpoints per cell. We conclude that microglia are necessary to regulate sleep after an immune challenge.</p>
Data from: Global trends of cropland phosphorus use and sustainability challenges
<p>To meet the growing food demand while addressing the multiple challenges of exacerbating phosphorus (P) pollution and depleting P rock reserves, P use efficiency (PUE, the ratio of productive P output to P input in a defined system) in crop production needs to be improved. Although many efforts have been devoted to improving nutrient management practices on farms, few studies have examined the historical trajectories of PUE and their socioeconomic and agronomic drivers on a national scale. Here we present a database of the P budget (the input and output of the crop production system) and PUE by country and by crop type for 1961–2019 and examine the substantial contribution of several drivers for PUE, such as economic development stages and crop portfolios. To address the P management challenges, we found that global PUE in crop production must increase to 68–81%, and recent trends indicate some meaningful progress towards this goal. However, P management challenges and opportunities in croplands vary widely among countries.</p>
Data and script for "Dispersal syndromes in challenging environments: a cross-species experiment"
<p>Datset and R scripts for all analysis of the manuscript "Dispersal syndromes in challenging environments: a cross-species experiment" published in Ecology Letters.</p>
Compensatory recruitment unlikely in high elevation amphibian populations challenged with disease
<p>Population-level variation in host responses to disease can be observed across a wide array of host-pathogen systems. Some host populations are extirpated, some persist at lower densities or abundances, and others rebound to pre-disease levels. Understanding the causes of this variation, and the mechanisms of successful persistence, can serve as vital information for species conservation. One such mechanism of population persistence that has gained support in the amphibian <em>Batrachochytrium dendrobatidis</em> (Bd) host-pathogen system is the demographic process of compensatory recruitment. Host populations may persist by increasing recruitment to compensate for reduced survival due to infection, thus limiting the negative effects of the disease on population trajectories. However, high-elevation amphibian populations are inherently vulnerable to stochastic processes and may be limited in their ability to exhibit compensatory recruitment relative to lower-elevation populations.</p> <p>We use long-term mark-recapture data from five populations of boreal toads (<em>Anaxyrus boreas boreas</em>), across an elevational gradient in Colorado, before and after pathogen arrival to assess whether populations can persist with Bd via compensatory recruitment.</p> <p>Prior to Bd arrival, we found a life history tradeoff between survival and recruitment across elevations, where high-elevation toads have high survival but lower recruitment and vice versa at lower elevations.</p> <p>Bd arrival had a strong negative effect on apparent annual survival and recruitment, and led to negative population growth rates and dramatically reduced host abundances. The data did not support the occurrence of compensatory recruitment.</p> <p>Synthesis and applications. Our unique dataset suggests that demographic responses to pathogens may be environmentally (i.e., elevationally) context-dependent and highlights the value of long-term monitoring. We recommend that practitioners verify that potential persistence mechanisms occur across multiple populations and relevant environmental gradients to counter any assumptions of the mechanism existing species-wide. Quantifying any variation in population responses to disease will aid in understanding the bounds of such persistence mechanisms, and identify particularly vulnerable populations where mechanisms are non-existent.</p>
EXPLORE Machine Learning Lunar Data Challenges 2022 - QGIS project
<p>This dataset contains the the EXPLORE Machine Learning Data Challenge 2022 QGIS project.</p> <p>The project embed the following Archytas Dome layers:</p> <p><strong>Raster</strong></p> <ul> <li>Narrow Angle Camera (NAC)</li> <li>DEM derived from NAC</li> <li>Slope computer on DEM</li> </ul> <p><strong>Vectorial</strong></p> <ul> <li>POIs - Points Of Interest to be used in STEP 3 </li> </ul> <p> </p> <p> </p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Images were processed from NASA PDS raw data using USGS ISIS and NASA ASP tools.</p>
Monitoring ML Systems: Challenges, Solutions and Metrics from a Practitioners' Perspective
<p>This is the dataset for the paper "Monitoring ML Systems: Challenges, Solutions and Metrics from a Practitioners' Perspective". The dataset is recorded in an MS Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.</p> <p>(1) <strong>Selected Projects (GitHub)</strong> contain the 15 selected ML projects with the URL of each project.</p> <p>(2) <strong>Raw Data (GitHub)</strong> contains the information about the randomly selected 600 issues, such as issue titles, issue links, issue id.</p> <p>(3) <strong>Raw Data (SO) </strong>contains the information about the randomly selected 2088 SO posts (part of the 2174 SO posts), such as post titles, post link, post id, and open date.</p> <p>(4) <strong>MLOps (SO) </strong>contains the information about the 86 MLOps SO posts (part of the 2174 SO posts), such as post titles, post link, post id, and open date.</p> <p>(5) <strong>Identified Challenges (GitHub) </strong>contain the list of categories, subcategories, and codes of identified challenges from GitHub issues.</p> <p>(6) <strong>Identified Solutions (GitHub) </strong>contain the list of categories, subcategories, and codes of identified solutions from GitHub issues.</p> <p>(7) <strong>Identified Metrics (GitHub) </strong>contain the list of identified metrics from GitHub issues.</p> <p>(8) <strong>Identified Challenges (SO) </strong>contain the list of categories, subcategories, and codes of identified challenges from SO posts.</p> <p>(9) <strong>Identified Solutions (SO) </strong>contain the list of categories, subcategories, and codes of identified solutions from SO posts.</p> <p>(10) <strong>Identified Metrics (SO) </strong>contain the lists of identified metrics from SO posts.</p> <p>(11) <strong>Identified Challenges (Interview)</strong> contain the list of categories, subcategories, and codes of identified challenges from interviews.</p> <p>(12) <strong>Identified Solutions (Interview) </strong>contain the list of categories, subcategories, and codes of identified solutions from interviews.</p> <p>(13) <strong>Identified Metrics (Interview) </strong>contain the list of identified metrics from interviews.</p> <p>(14) <strong>Identified Challenges (Final) </strong>contain the taxonomy of the final challenges identified from GitHub issues, SO posts, and interviews.</p> <p>(15) <strong>Identified Solutions (Final) </strong>contain the<strong> </strong>taxonomy of the final solutions identified from GitHub issues, SO posts, and interviews.</p> <p>(16) <strong>Identified Metrics (Final) </strong>contain the<strong> </strong>final list of metrics identified from GitHub issues, SO posts, and interviews.</p>
Dataset for IDRC Project: Exploring the opportunities and challenges of implementing open research strategies within development institutions. International Development Research Center.
<p>Data Package: Exploring the opportunities and challenges of implementing open research strategies within development institutions</p> <p>DOI for this package: https://doi.org/10.5281/zenodo.844394<br> Project Description: https://doi.org/10.3897/rio.2.e8880<br> Data Management Plan: https://doi.org/10.3897/rio.3.e14672<br> Other Related Documents and Reports: https://riojournal.com/collection/18/<br> Funder: International Development Research Centre/Centre de rechereches pour le développement international, https://doi.org/10.13039/501100000193</p> <p><br> Abstract<br> ========</p> <p>This is the Data Package for the project "Exploring the opportunities and challenges of implementing open research strategies within development institutions" the proposal for which was published as https://doi.org/10.3897/rio.2.e8880. The research project conducted open data pilot case studies with seven IDRC grantees to develop and implement open data management and sharing plans. The results of the case studies served to refine guidelines for the implementation of development research funders’ open research data policies.</p> <p>Contents<br> ========</p> <p>The Data Package contains all the public data generated by the project. The package was curated and metadata generated, including an HTML Catalog using the Calcyte Tool (https://codeine.research.uts.edu.au/eresearch/calcyte) developed at University of Technology Sydney.</p> <p>The project had two major phases:</p> <p>1. A review, based on desk work and interviews with data management experts<br> 2. Case studies, based on implementing open data practices within seven IDRC funded research projects</p> <p>Review<br> ------</p> <p>The review, published at https://riojournal.com/article/14673/ was supported by desk work and interviews. The materials related to the interviews can be found in the directory:</p> <p>* Policy and Implementation Review Interviews</p> <p>Case Studies<br> ------------</p> <p>Seven IDRC-funded projects were contributed to the pilot project.</p> <p>The materials generated by the case studies and used to support the final report (to be published with the collection at https://riojournal.com/collection/18/) are found in the following directories.</p> <p>* Introductory_Data_Workshop_Materials<br> * Introductory_Workshop_Presentations<br> * Data Management Planning<br> * SciDataCon Presentations<br> * Final_Project_Workshop_Materials<br> * Final_Project_Workshop_Presentations</p> <p>The files are encoded with a three letter code that identifies the relevant contributing project in each case. The contributing projects were:</p> <p>* Crowd Sourcing Data to fight Social Crimes: Harassmap, Egypt (HMP)<br> * The Brazilian Virtual Herbarium: CRIA, Brazil(BVH)<br> * Strengthening the Economic Committee of the National Assembly in Vietnam: Centre for Analysis and Forecasting, Vietnam (ECV)<br> * The Impact of Copyright User Rights: Derechos Digitales, Columbia (DED)<br> * Establishing a clearinghouse for tobacco economic data in Africa: DataFirst, South Africa (TED)<br> * Les problèmes négligés des systèmes de santé en Afrique : une incitation aux réformes: LASDEL, Niger (NDF)<br> * Indigenous Knowledge in Climate Change: Natural Justice, South Africa (IKC)</p> <p>More details will be found in the Case Studies and in the Final Report (forthcoming at https://riojournal.com/collection/18/)</p> <p>References<br> ==========</p> <p>* Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880<br> * Neylon C (2017) Data Management Plan: IDRC Data Sharing Pilot Project. Research Ideas and Outcomes 3: e14672. https://doi.org/10.3897/rio.3.e14672<br> * Neylon C, Chan L (2016-17) Exploring the opportunities and challenges of implementing open research strategies within development institutions: A project of the International Development Research Center, Research Ideas and Outcomes Collection, https://riojournal.com/collection/18/</p> <p> </p>
2018 IEEE IUS SA-VFI Challenge
<p>The 2018 IEEE IUS SA-VFI challenge consist on estimating blood flow velocities from both simulated and measured ultrasound RF element data sets. The challenge results are meant to be presented during the next IEEE International Ultrasonics Symposium 2018 in Kobe (Japan).</p> <p>The data sets for the challenge consist on pre-beamformed RF element signals using a pre-selected synthetic aperture (SA) sequence on flow phantoms, both simulations and experiments. The details of the provided data sets and example code is given in the main document "2018_IEEE_IUS_SA-VFI_Challenge.pdf". </p> <p>A website for the participants with additional information is available at https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2018/. </p> <p> </p> <p> </p>
RMLStreamer supported by RML-view-to-CSV in the performance track of the KGCW Challenge 2024
<p>This repository contains all necessary data to reproduce the participation of RMLStreamer with support of RML-view-to-CSV in the <a href="../records/10721875">KGCW 2024 Challenge</a>, as well as the reported results.</p> <p>For more information we refer to the related paper:</p> <p><em>E. de Vleeschauwer, B. De Meester, RMLStreamer supported by RML-view-to-CSV in the performance track of the KGCW Challenge 2024, in: Proceedings of the 5th International Workshop on Knowledge Graph Construction (KGCW 2024) co-located with 21th Extended Semantic Web Conference (ESWC 2024), 2024</em></p> <p> </p>
Subset of global model sea level data for "Challenges, Advances and Opportunities in Regional Sea Level Projections: the Role of Ocean-shelf Dynamics"
<p>Monthly sea surface height above the geoid data in NW European seas from six global simulations using the NEMO ocean model (https://www.nemo-ocean.eu/) for 1990 to 2009</p> <p><strong>ORCA0083_DFS_NWS_ssh_1990_2009, ORCA025_DFS_NWS_ssh_1990_2009, ORCA1_DFS_NWS_ssh_1990_2009,</strong> are the N006 simulation set created by Andrew Coward and the NOC Marine Systems Modelling team as used by:</p> <p>Baker et al 2022 Biological Carbon Pump Sequestration Efficiency in the North Atlantic: A Leaky or a Long-Term Sink? Global Biogeochemical Cycles <a href="https://doi.org/10.1029/2021GB007286">https://doi.org/10.1029/2021GB007286</a>,</p> <p>Wilson, C. <em>et al.</em> 2021 Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. <em>Commun. Earth Environ.</em> <strong>2</strong>, 164. <a href="https://doi.org/10.1038/s43247-021-00237-0">https://doi.org/10.1038/s43247-021-00237-0</a> (2021).</p> <p>These simulations are forced by the Drakkar Forcing Set 5.2 (DFS) and initialised at 1958, with a nominal 1/12, 1/4 and 1 degree resolution. See references for further model details.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009, ORCA025_JRA_tides_NWS_ssh_1990_2009, ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009, </strong>are new simulations produced by Chris Wilson, James Harle and the Shelf Enabled NEMO team. All are forced by the JRA reanalysis, initialised in 1976.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009</strong> is a reference run based on GO9, an evolution of the Joint Marine Modelling Programme configuration described by Storkey et al 2018 UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geoscientific Model Development https://gmd.copernicus.org/articles/11/3187/2018/</p> <p><strong>ORCA025_JRA_tides_NWS_ssh_1990_2009</strong> adds explicit tides to this.</p> <p><strong>ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009</strong> adds tides, Generic Length Scale Mixing and Multi-envelope vertical coordinates</p> <p>Details of these simulations can be found here:</p> <p>https://github.com/NOC-MSM/SE-NEMO </p>
Challenges to Profitability for Energy Storage in High Renewable Energy Systems
<p>This figure illustrates the impact of renewable energy source (RES) penetration on electricity price spreads and the profitability of energy storage systems.</p> <p>In scenarios with a low share of RES, the price spread is significant. During low-demand periods, electricity is predominantly supplied by cost-effective RES, leading to lower prices. Conversely, during high-demand hours, traditional power plants with higher operational costs are required, resulting in elevated prices. Energy storage systems can capitalize on this large price spread by charging during low-price periods and discharging during high-price periods, thereby maximizing their profits.</p> <p>In contrast, with a high share of RES, both low and high-demand periods are largely covered by renewable sources. This extensive reliance on RES minimizes the price differential between these periods, resulting in a much smaller price spread. Consequently, the potential for storage systems to profit from price arbitrage is reduced, as the opportunities to buy low and sell high diminish.</p>
OpenEarthMap Land Cover Mapping Few-Shot Learning Challenge
<pre><strong>***The challenge is over, please use this verion for post-challenge research. This version contains all the data and files, <br>except the labels of the query-set of the testset that are withheld and researchers can submit the predictions at the <br>challenge leaderboard for evaluation***</strong></pre> <p><strong>Overview</strong></p> <p>This challenge is co-organized with the <a href="https://sites.google.com/view/l3divu2024/overview" target="_blank" rel="noopener">L3D-IVU 2024 CVPR</a> workshop. The challenge is an extension of the <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap </a>benchmark dataset for a generalized few-shot semantic segmentation (GFSS) task. The challenge aims to evaluate and benchmark learning methods for few-shot semantic segmentation on the OpenEarthMap dataset to promote research on geoinformatics for social good. The motivation is to enable researchers to develop few-shot learning algorithms for high-resolution RS image semantic segmentation.</p> <p><strong>Page</strong></p> <p><a href="https://cliffbb.github.io/OEM-Fewshot-Challenge/" target="_blank" rel="noopener">https://cliffbb.github.io/OEM-Fewshot-Challenge/</a></p> <p><strong>Baseline</strong></p> <p>The baseline model for the challenge is available <a href="https://github.com/cliffbb/OEM-Fewshot-Challenge" target="_blank" rel="noopener">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>The <a href="https://codalab.lisn.upsaclay.fr/competitions/19210" target="_blank" rel="noopener">challenge leaderboard </a>is opened for post-challenge research to enable researchers to evaluate the predictions on the query-set of the testset.</p> <p><strong>Description</strong></p> <p>The dataset has been designed for remote sensing few-shot learning, particularly, for GFSS tasks in land cover mapping. The dataset consists of only 408 samples from the original <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap</a> dataset for RS image semantic segmentation. It extends the original 8 semantic classes of the OpenEarthmap benchmark to 15 classes, which is split into 7:4:4 for <em>train_base_class</em>, <em>val_novel_class</em>, and <em>test_novel_class</em> disjointed sets, respectively (i.e., <em>train_base_class</em> ∩ <em>val_novel_class</em> ∩ <em>test_novel_class</em> = ∅). The 408 samples are also split into 258 as `trainset`, 50 as `valset`, and 100 as `testset`. The `trainset` is for pre-training a backbone network. It contains only the images and labels of the <em>train_base_class</em> split. Both the `valset` and the `testset` consist of a <em>support set</em> and a <em>query set</em> for a <strong>5-shot</strong> with <strong>4 novel classes</strong> and <strong>7 base classes</strong> GFSS task. The `valset` and the `testset` contain the images and labels of the <em>val_novel_class</em> and the <em>test_novel_class</em> splits, respectively.</p> <p>The challenge is in two phases: development phase and evaluation phase. The `valset` is for the development phase and the `testsets` is for the evaluation phase. Both `valset` and `testset` have 20 image-label pair examples, 5-set examples for each of the 4 novel classes in the <em>support set</em>. The `valset` and the `testset` contain an additional 30 images and 80 images, respectively, in the <em>query set</em>, which are to be predicted using the 20 labelled images in their <em>support set</em>. The labels for each image in the <em>support sets</em> do not contain any of the <em>train_base_class</em> split. Also, in each 5-set examples, the labels contain only one novel class (i.e., one novel class per 5-set examples). However, in the `valset`, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>val_novel_class</em>; and in the `testset, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>test_novel_class</em> `. Note that both the <em>support set</em> and <em>query set</em> in the `valset` are different from the ones in the `testset`.</p> <pre><br>File Structure and Content (All files are in `.tif` format): ----------------------------------------------------------- 1. **trainset.zip**: - Contains `images` and `labels` folders - `images` folder: 258 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 258 segmentation masks of the images in the `images` folder. 2. **valset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 50 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 30 ``query set`` images in the `images` folder are withheld.<br>3. **testset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 100 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 80 ``query set`` images in the `images` folder are withheld.<br> 4. **train.txt**:<br> - Contains a list of file names in the `trainset.zip`.<br> <br>3. **val.json** and **test.json**:<br> - Contains a list of file names the in the `valset.zip` and `testset.zip`, respectively. Below is<br> the structure of the `val.json` and `test.json` files.<br> - fnames = {<br> {"support_set": {8: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 9: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 10: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 11: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"]},<br> {"query_set": ["filename_1.tif", "filename_2.tif", "filename_3.tif", ...<br> ...., <br> "filename_n.tif"]} <br> }<br> Land Cover Mapping Classes Strucure: ------------------------------------<br>1. **The `trainset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> }<br><br>2. **The `valset` and `testset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> # ***Novel classes***<br> 8: '',<br> 9: '',<br> 10: '',<br> 11: ''<br> }<br><br> - The class names for the ***Novel classes*** depends on the data set.<br><br> For the `valset`, the class names can be updated as:<br> {<br> 8: 'road type 2',<br> 9: 'river',<br> 10: 'boat & ship',<br> 11: 'agric land type 2'<br> }<br><br> For the `testset`, the class names can be updated as:<br> {<br> 8: 'vehicle & cargo-trailer',<br> 9: 'parking space',<br> 10: 'sports field',<br> 11: 'building type 2'<br> }<br><br><strong>License</strong></pre> <p>See <a href="../records/7223446" target="_blank" rel="noopener">OpenEarthMap</a></p>
Fig 2 in Opportunities and its challenges in fish production: The case of Lake Koka, East Showa Zone, Oromia National Regional State, Ethiopia
Fig 2: Expansion of water hyacinth in Lume district at Koka Lake, February, 2020
Fig 1 in Opportunities and its challenges in fish production: The case of Lake Koka, East Showa Zone, Oromia National Regional State, Ethiopia
Fig 1: Targeted fish species at Lake Koka
Training Dataset for HNTSMRG 2024 Challenge
<h1>Training Dataset for HNTSMRG 2024 Challenge</h1> <h2> </h2> <h2>Overview</h2> <p>This repository houses the publicly available training dataset for the <a href="https://hntsmrg24.grand-challenge.org/" target="_blank" rel="noopener">Head and Neck Tumor Segmentation for MR-Guided Applications (HNTSMRG) 2024 Challenge</a>.</p> <p>Patient cohorts correspond to patients with histologically proven head and neck cancer who underwent radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. The cancer types are predominately oropharyngeal cancer or cancer of unknown primary. Images include a pre-RT T2w MRI scan (1-3 weeks before start of RT) and a mid-RT T2w MRI scan (2-4 weeks intra-RT) for each patient. Segmentation masks of primary gross tumor volumes (<em>abbreviated GTVp</em>) and involved metastatic lymph nodes (<em>abbreviated GTVn</em>) are provided for each image (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1283110/" target="_blank" rel="noopener">derived from multi-observer STAPLE consensus</a>).</p> <p>HNTSMRG 2024 is split into 2 tasks:</p> <ul> <li><strong>Task 1</strong>: Segmentation of tumor volumes (GTVp and GTVn) on <strong>pre-RT MRI</strong>.</li> <li><strong>Task 2</strong>: Segmentation of tumor volumes (GTVp and GTVn) on <strong>mid-RT MRI</strong>.</li> </ul> <p>The same patient cases will be used for the training and test sets of both tasks of this challenge. Therefore, we are releasing a single training dataset that can be used to construct solutions for either segmentation task. <u>The test data provided (via Docker containers), however, will be different for the two tasks.</u> Please consult the <a href="https://hntsmrg24.grand-challenge.org/dataset/" target="_blank" rel="noopener">challenge website for more details</a>.</p> <p> </p> <h2>Data Details </h2> <ul> <li>DICOM files (images and structure files) have been converted to NIfTI format (<em>.nii.gz</em>) for ease of use by participants via <a href="https://pubmed.ncbi.nlm.nih.gov/33607331/" target="_blank" rel="noopener">DICOMRTTool </a>v. 1.0.</li> <li>Images are a mix of fat-suppressed and non-fat-suppressed MRI sequences. Pre-RT and mid-RT image pairs for a given patient are consistently either fat-suppressed or non-fat-suppressed.</li> <li>Though some sequences may appear to be contrast enhancing, <em>no exogenous contrast is used</em>. </li> <li>All images have been manually cropped from the top of the clavicles to the bottom of the nasal septum (~ oropharynx region to shoulders), allowing for more consistent image field of views and removal of identifiable facial structures.</li> <li>The mask files have one of three possible values: <strong><em>background = 0, GTVp = 1, GTVn = 2</em></strong> (in the case of multiple lymph nodes, they are concatenated into one single label). This labeling convention is <a href="https://hecktor.grand-challenge.org/Data/" target="_blank" rel="noopener">similar to the 2022 HECKTOR Challenge</a>. </li> <li><strong>150 unique patients</strong> are included in this dataset. Anonymized patient numeric identifiers are utilized.</li> <li>The entire training dataset is <strong>~15 GB</strong>. </li> </ul> <p> </p> <h2>Dataset Folder/File Structure</h2> <p>The dataset is uploaded as a ZIP archive. Please unzip before use. NIfTI files conform to the following standardized nomenclature: <em>ID_timepoint_image/mask.nii.gz</em>. For mid-RT files, a "registered" suffix (<em>ID_timepoint_image/mask_registered.nii.gz</em>) indicates the image or mask has been registered to the mid-RT image space (<em>see more details in Additional Notes below</em>).</p> <p>The data is provided with the following folder hierarchy:</p> <ul> <li><em><strong>Top-level folder </strong>(named "HNTSMRG24_train")</em> <ul> <li><em><strong>Patient-level folder</strong> (anonymized patient ID, example: "2") </em> <ul> <li><em><strong>Pre-radiotherapy data folder</strong> ("preRT") </em> <ul> <li>Original pre-RT T2w MRI volume (<em>example: "2_preRT_T2.nii.gz"</em>).</li> <li>Original pre-RT tumor segmentation mask (<em>example: "2_preRT_mask.nii.gz"</em>).</li> </ul> </li> <li><em><strong>Mid-radiotherapy data folder</strong></em> <em>("midRT")</em> <ul> <li>Original mid-RT T2w MRI volume (<em>example: "2_midRT_T2.nii.gz"</em>).</li> <li>Original mid-RT tumor segmentation mask (<em>example: "2_midRT_mask.nii.gz"</em>).</li> <li>Registered pre-RT T2w MRI volume (<em>example: "2_preRT_T2_registered.nii.gz"</em>).</li> <li>Registered pre-RT tumor segmentation mask (<em>example: "2_preRT_mask_registered.nii.gz"</em>).</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><em>Note</em>: Cases will exhibit variable presentation of ground truth mask structures. For example, a case could have only a GTVp label present, only a GTVn label present, both GTVp and GTVn labels present, or a completely empty mask (i.e., complete tumor response at mid-RT). <strong>The following case IDs have empty masks at mid-RT (indicating a complete response): 21, 25, 29, 42.</strong> These empty masks are <em>not errors</em>. There will similarly be some cases in the test set for Task 2 that have empty masks. </p> <p> </p> <h2>Details Relevant for Algorithm Building</h2> <ul> <li>The goal of Task 1 is to generate a pre-RT tumor segmentation mask (e.g., "<em>2_preRT_mask.nii.gz"</em><em> </em>is the relevant label). During blind testing for Task 1, only the pre-RT MRI (e.g., "<em>2_preRT_T2.nii.gz</em>") will be provided to the participants algorithms. </li> <li>The goal of Task 2 is to generate a mid-RT segmentation mask (e.g., "<em>2_midRT_mask.nii.gz"</em><em> </em>is the relevant label). During blind testing for Task 2, the mid-RT MRI (e.g., "<em>2_midRT_T2.nii.gz</em>"), original pre-RT MRI (e.g., "<em>2_preRT_T2.nii.gz</em>"), original pre-RT tumor segmentation mask (e.g., "<em>2_preRT_mask.nii.gz</em>"), registered pre-RT MRI (e.g., "<em>2_preRT_T2_registered.nii.gz</em>"), and registered pre-RT tumor segmentation mask (e.g., "<em>2_preRT_mask_registered.nii.gz</em>") will be provided to the participants algorithms. </li> <li>When building models, the resolution of the generated prediction masks should be the same as the corresponding MRI for the given task. In other words, the generated masks should be in the correct pixel spacing and origin with respect to the original reference frame (i.e., pre-RT image for Task 1, mid-RT image for Task 2). More details on the submission of models will be located on the <a href="https://hntsmrg24.grand-challenge.org/submission-instructions/" target="_blank" rel="noopener">challenge website</a>. </li> </ul> <p> </p> <h2>Additional Notes</h2> <ol> <li><em>General notes.</em> <ol> <li>NIfTI format images and segmentations may be easily visualized in any NIfTI viewing software such as <a href="https://www.slicer.org/" target="_blank" rel="noopener">3D Slicer</a>.</li> <li><strong>Test data will not be made public until the completion of the challenge</strong>. The complete training and test data will be published together (along with all original multi-observer annotations and relevant clinical data) at a later date via The Cancer Imaging Archive. Expected date ~ Spring 2025.</li> </ol> </li> <li><em>Task 1 related notes.</em> <ol> <li>When training their algorithms for Task 1, participants can choose to use only pre-RT data or add in mid-RT data as well. Initially, our plan was to limit participants to utilizing only pre-RT data for training their algorithms in Task 1. However, upon reflection, we recognized that in a practical setting, individuals aiming to develop auto-segmentation algorithms could theoretically train models using any accessible data at their disposal. Based on current literature, we actually don't know what the best solution would be! Would the incorporation of mid-RT data for training a pre-RT segmentation model actually be helpful, or would it merely introduce harmful noise? The answer remains unclear. Therefore, we leave this choice to the participants.</li> <li><em>Remember, though, during testing, you will ONLY have the pre-RT image as an input to your model </em>(naturally, since Task 1 is a pre-RT segmentation task and you won't know what mid-RT data for a patient will look like).</li> </ol> </li> <li><em>Task 2 related notes.</em> <ol> <li>In addition to the mid-RT MRI and segmentation mask, we have also provided a registered pre-RT MRI and the corresponding registered pre-RT segmentation mask for each patient. We offer this data for participants who opt not to integrate any image registration techniques into their algorithms for Task 2 but still wish to use the two images as a joint input to their model. Moreover, in a real-world adaptive RT context, such registered scans are typically readily accessible. Naturally, participants are also free to incorporate their own image registration processes into their pipelines if they wish (or ignore the pre-RT images/masks altogether). <ol> <li>Registrations were generated using <a href="https://pypi.org/project/SimpleITK/" target="_blank" rel="noopener">SimpleITK</a>, where the mid-RT image serves as the fixed image and the pre-RT image serves as the moving image. Specifically, we utilized the following steps: 1. Apply a centered transformation, 2. Apply a rigid transformation, 3. Apply a deformable transformation with Elastix using a preset parameter map (<a href="https://github.com/SuperElastix/ElastixModelZoo/tree/master/models/Par0023" target="_blank" rel="noopener">Parameter map 23 in the Elastix Zoo</a>). This particular deformable transformation was selected as it is open-source and was benchmarked in a previous similar application (<a href="https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.16128" target="_blank" rel="noopener">https://doi.org/10.1002/mp.16128</a>). For cases where excessive warping was noted during deformable registration (a small minority of cases), only the rigid transformation was applied. </li> </ol> </li> </ol> </li> </ol> <p> </p> <h2>Contact</h2> <p>We have set up a general email address that you can message to notify all organizers at: <a href="mailto:hntsmrg2024@gmail.com">hntsmrg2024@gmail.com</a>. Additional specific organizer contacts:</p> <ul> <li>Kareem A. Wahid, PhD (<em>kawahid@mdanderson.org</em>)</li> <li>Cem Dede, MD (<em>cdede@mdanderson.org</em>)</li> <li>Mohamed A. Naser, PhD (<em>manaser@mdanderson.org</em>)</li> </ul>
Data from: Challenging trophic position assessments in complex ecosystems: calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter. A case study from Marquesas Islands coral reefs
<p>Assessments of ecosystem functioning are a fundamental ecological challenge and an essential foundation for ecosystem-based management. An understanding of species trophic positions (TP) is essential to characterize food web architecture. However, despite the intuitive nature of the concept, empirically estimating TP is a challenging task due to the complexity of trophic interaction networks. Various alternative methods are proposed to assess TPs, including different approaches to account for the different sources of organic matter at the base of the food web (the "baseline"). However, it is often not clear which methodological approach and which baseline choices are the most reliable. Using an ecosystem-wide assessment of a tropical reef (Marquesas Islands, French Polynesia, with available data for 70 coral reef invertebrate and fish species), we tested whether different commonly used TP estimation methods yield similar results and, if not, whether it is possible to identify the most reliable method. We found significant differences in TP estimates of up to 1.7 TP for the same species, depending on the method and the baseline used. When using bulk stable isotope data, the choice of the baseline significantly impacted TP values. Indeed, while δ<sup>15</sup>N values of macroalgae led to consistent TP estimates, those using phytoplankton generated unrealistically low TP estimates. The use of a conventional enrichment factor (i.e. 3.4 ‰) or a "variable" enrichment factor (i.e. according to feeding guilds) also produced clear discrepancies between TP estimates. Regarding the use of different calculation methods, TPs obtained with δ<sup>15</sup>N values of source amino acids (compound specifics isotope analysis) were close to those assessed with macroalgae, but evidenced the opposite seasonal pattern, with significantly lower TPs in winter than in summer for the majority of assessed species, with particularly pronounced differences for lower TP species. We use the observed differences to discuss possible drivers of the diverging TP estimates and the potential ecological implications.</p>
Adsorption of drugs on nanoplastics: Modeling challenges and experimental proof - datasets and structures
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