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2,863 results for “Challenge”
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
Exploring opportunities and challenges for intangible cultural heritage at the intersection of institutions, civil society, and migrant communities
<p>This data set was produced in the CultureLabs project to explore the needs and expectations of migrant and refugee communities in several European countries in relation to communicating and sharing their intangible cultural heritage (ICH) practices as well as of cultural and civil society organisations that plan to support this. </p> <p>The data was gathered across Europe in early 2019 and consists of 101 surveys administered and 27 interviews. 101 surveys were administered to people belonging to migrant and refugee communities in several hosting countries, mostly Italy and Germany, while the countries of origin of respondents covered four continents. The survey consisted of closed-ended questions and was structured into eight thematic areas related to a balanced mix of independent and dependent variables for the generation of correlations and clusters. The thematic areas included: background information; employment status; socioeconomic status; residency or migration status; individual preferences, attitudes, and activities; intercultural awareness; extent of interest and preparedness to become actively involved in cultural activities; and access to and use of digital tools. 27 semi-structured interviews were also conducted. The interviewees were recruited from the collaborative network of the nine partner institutions in the CultureLabs project. Although each interview gave space to the issues raised by the interviewees, four themes were deepened with the majority of participants: interest in cultural activities, expectations from cultural activities, the importance of digital tools for cultural activities, and the relevance of cultural heritage activities for individuals and their socio-cultural integration in the host society.</p> <p>The findings resulted in a peer-reviewed publication available at the following link: <a href="https://www.tandfonline.com/doi/full/10.1080/13527258.2021.1922934">https://www.tandfonline.com/doi/full/10.1080/13527258.2021.1922934</a> </p> <p>The original dataset has been processed by removing details and parts that might compromise the anonymity of the participant. </p> <p>This work is supported by the European Union’s Horizon2020 project CultureLabs: Recipes for Social Innovation, under Grant Agreement 770158. The sole responsibility of this work lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.</p> <p> </p> <p> </p>
Survey on Understanding the Challenges of Accessing Water in Metro Manila
<p>Conducted in 2020, the results of the household survey (n=800) describe the challenges of accessing water by urban poor communities living in informal and resettlement sites in and around Metro Manila. Responses provide information on experiences related to price and availability of water, perceptions about the performance of water providers, coping strategies during water shortages, housing and sanitation conditions, and perceptions about the role and influence of state and non-state players related to water provision.</p>
Mapping of glacial lakes using Sentinel-1 and Sentinel-2 data and a random forest classifier: Strengths and challenges
<p>The water body detection and mapping algorithm named 'glakemap' that I designed was aimed at specifically mapping glacial lakes across alpine regions where their detection and mapping are challenged by many factors such as shadows, cloud cover, turbidity, and ice surface. The algorithm uses Copernicus Sentinel-1 and -2 satellites data and machine learning model (random forest) in an integrated manner to automatically classify glacial lakes from other surface features. In specific, the algorithm takes Sentinel-1 and -2 satellites data as the main inputs. It calculates radar backscatter and Normalised Difference Water Indices (NDWIs) using these datasets, respectively. The radar backscatter and NDWIs products (images) are segmented using a set of rules producing many polygons including lake polygons. Lake polygons are then automatically separated/retained using the random forest model which is trained using features relevant to lakes.</p> <p>The dataset is also available at https://www.mountcryo.org/</p>
Same Side Stance Classification Challenge
<p>The dataset contains argument pairs which are sampled from <a href="https://zenodo.org/record/4139439#.X-C6nOG-q6M">args.me dataset</a> and cover two topics: abortion and gay marriage. The dataset is used in the <a href="http://sameside.webis.de/">same side stance classification challenge</a> which consists of two experiments (cross-topics and within topics).</p> <p>Cite the dataset as <a href="https://webis.de/publications.html#stein_2021">Benno Stein, Yamen Ajjour, Roxanne El Baff, Khalid Al-Khatib, Philipp Cimiano, and Henning Wachsmuth. Same Side Stance Classification. In Yamen Ajjour et al, editors, Same Side Stance Classification Shared Task 2019, volume 2921, July 2021</a></p>
Data of "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?"
<p>Data for article "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?" Dataset of 64 crossbred beef heifers which were subjected to three ACTH challenges. Both experimental independent variables (animal id, horn status, replicate, time of day of the ACTH challenge, ACTH challenge number) and post-ACTH salivary cortisol concentrations (at the seven sampling timepoints and area under the curve values) are presented.</p>
Challenges and opportunities of species distribution modelling of terrestrial arthropod predators
<p>Aim. Species distribution models (SDMs) have emerged as essential tools in the equipment of many ecologists, useful to explore species distributions in space and time and answering an assortment of questions related to biogeography, climate change biology and conservation biology. Historically, most SDM research concentrated on well-known organisms, especially vertebrates. In recent years, these tools are becoming increasingly important for predicting the distribution of understudied invertebrate taxa. Here, we reviewed the literature published on main terrestrial arthropod predators (ants, ground beetles and spiders) to explore some of the challenges and opportunities of species distribution modelling in mega-diverse arthropod groups. Location. Global. Methods. Systematic mapping of the literature and bibliometric analysis. Results. Most SDM studies of animals to date have focused either on broad samples of vertebrates or on arthropod species that are charismatic (e.g. butterflies) or economically important (e.g. vectors of disease, crop pests and pollinators). We show that the use of SDMs to map the geography of terrestrial arthropod predators is a nascent phenomenon, with a near-exponential growth in the number of studies over the past 10 years and still limited collaborative networks among researchers. There is a bias in studies towards charismatic species and geographical areas that hold lower levels of diversity but greater availability of data, such as Europe and North America. Conclusions. Arthropods pose particular modelling challenges that add to the ones already present for vertebrates, but they should also offer opportunities for future SDM research as data and new methods are made available. To overcome data limitations, we illustrate the potential of modern data sources and new modelling approaches. We discuss areas of research where SDMs may be combined with dispersal models and increasingly available phylogenetic and functional data to understand evolutionary changes in ranges and range-limiting traits over past and contemporary time scales.</p>
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
GENEA Challenge 2022 User Study Raw Responses
<p>This archive contains user-study response data in JSON format from the GENEA Challenge 2022 (https://youngwoo-yoon.github.io/GENEAchallenge2022/). Included in this data are the raw text responses on the questionnaire that was presented to participants after finishing the evaluation.</p> <p>If you use this material, please cite the following 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> </p>
Madagascar's fire regimes challenge global assumptions about landscape degradation
<p><span><span>Fire and environmental dataset (2003 - 2019) for Phelps et al. (2022, Global Change Biology). <br>Associated manuscript abstract: Narratives of landscape degradation are often linked to unsustainable fire use by local communities.</span><span> Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity seen as threatened by unsustainable anthropogenic </span><span>fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we apply a comparative approach using MODIS remote sensing data (2003-2019), to determine relationships between Madagascar's fire regimes and global patterns and trends. We demonstrate that Madagascar's fire regimes are similar to 88% of tropical burned area, with shared climate and vegetation characteristics. Therefore, rather than a global exception, Madagascar's fire regimes could usefully be understood as a microcosm of most tropical fire regimes, which contribute to global understanding of fire. We found that landscape-scale fire declined in grassy biomes across the tropics, and at a relatively fast rate on Madagascar. The island's high tree loss anomalies (1.25 to 4.77x the tropical average) were not explained by any general expansion of grassy biome burning and were centred in forests rather than at forest-savanna boundaries, demonstrating that high rates of forest degradation were not explained by landscape-scale fire escaping from savannas into forests. Associated with forests, landscape-scale fire trends reflected important differences among tropical regions, indicating a need to better understand regional variation in the anthropogenic drivers of change. Unexpectedly, the highest tree loss anomalies on Madagascar were centred in environments </span><span>without </span><span>landscape-scale fire, where the role of small-scale fires (<21ha) is unknown. Madagascar's fire regimes thus contribute two lessons with global implications: first, landscape-scale burning is declining in grassy biomes across the tropics and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with forest loss, indicating a need for more socio-ecological context around narratives of tropical fire and ecosystem degradation. </span></span></p>
Methodological challenges in the genomic analysis of an endangered mammal population with low genetic diversity
<p><span>Recently, populations of various species with very low genetic diversity have been discovered. Some of these persist in the long term, but others could face extinction due to accelerated loss of fitness. In this work, we characterize 45 individuals of one of these populations, belonging to the Iberian desman (<em>Galemys</em> <em>pyrenaicus</em>). For this, we used the ddRADseq technique, which generated 1,421 SNPs. The heterozygosity values of the analyzed individuals were among the lowest recorded for mammals, ranging from 26 to 91 SNPs/Mb. Furthermore, the individuals from one of the localities, highly isolated due to strong barriers, presented extremely high inbreeding coefficients, with values above 0.7. Under this scenario of low genetic diversity and elevated inbreeding levels, some individuals appeared to be almost genetically identical. We used different methods and simulations to determine if genetic identification and parentage analysis were possible in this population. Only one of the methods, which does not assume population homogeneity, was able to identify all individuals correctly. Therefore, genetically impoverished populations pose a great methodological challenge for their genetic study. However, these populations are of primary scientific and conservation interest, so it is essential to characterize them genetically and improve genomic methodologies for their research.</span></p>
Wildlife response to disturbances in agent-based models: synthesis on current development and challenges (Systematic Review)
<p>This dataset was obtained from a systematic screening of 124 articles presenting agent-based models of wildlife response to disturbances. It is part of a manuscript for submission to the journal Methods in Ecology and Evolution. The manuscript is entitled: Wildlife response to disturbances in agent-based models: synthesis on current development and challenges.</p>
Challenges and directions for open ecosystems biodiversity restoration: An overview of the techniques applied for Cerrado
<p>1. Ecological restoration of tropical open ecosystems remains challenging for both science and practice. Over the last decade, innovative techniques have been developed, but whether they have been successful or not remains to be demonstrated. Assessing the outcomes of these initiatives is crucial to drive the following steps to improve tropical grasslands and savanna restoration.</p> <p>2. Analyzing 82 data sets from the literature and primary data collection, we assessed the effectiveness of passive and active restoration techniques applied in Cerrado open ecosystems. We used plant diversity variables (species and growth forms) as indicators, considering ruderals and exotics as non-target species. Specifically, we aimed to answer: (i) How does the diversity of target species change through time in areas subject to passive restoration? (ii) Are active and passive restoration techniques effective in restoring the proportion of target species found in old-growth reference ecosystems? (iii) Have the current techniques been successful in recovering the proportions of growth forms of reference ecosystems?</p> <p>3. We found that target species proportions do not increase with time, suggesting limitations of typical species to colonize degraded sites. Hence, passive restoration will promote the conservation of a limited and constant number of target species. This number will depend on the magnitude of degradation and previous land use.</p> <p>4. The restoration techniques currently applied to restore the biodiversity of Cerrado open ecosystems are not reaching the reference standards, with distinct techniques driving plant communities to different sets of growth forms. Active restoration based on propagules obtained from pristine donor sites (topsoil translocation, plant material transplant, and seeding) performed better than passive restoration for most of the growth forms analyzed.</p> <p>5. Synthesis and Applications: Different growth forms have different roles in determining the structure and functioning of Cerrado vegetation. A mix of techniques can better approximate plant diversity and the proportionality of target species of pristine ecosystems. Singular restoration approaches are insufficient for restoring Cerrado open ecosystem biodiversity. Mixed efforts encompassing various techniques are required instead. Furthermore, it is likely restoration success can be improved with greater investment in improving our understanding of and developing existing restoration techniques.</p>
Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material
<p>This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation" (also included in this repository). </p> <p>Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages, and possible issues. This paper aims to study strategies, benefits, and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and<br> evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible benefits to foster app store-inspired elicitation among practitioners.</p> <p>The package contains the following files:</p> <p>1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution.</p> <p>2. Codebooks:<br> 2.a. Codebook Strategies: codebook of the strategies to select apps<br> 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2)<br> 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2)<br> 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE</p> <p>3. Labelled Data <br> 3.a Strategies - labeled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for the explanation of the themes).<br> 3.b Benefits IBE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for the explanation of the themes).<br> 3.c Challenges IBE - labeled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for the explanation of the themes).<br> 3.d Benefits ASE - labeled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for the explanation of the themes).<br> 3.e Challenges ASE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for the explanation of the themes).</p> <p>4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections).</p> <p>5. SLR data: data related to the lightweight systematic literature review <br> 5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks.<br> 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers. </p> <p>6. Readme.txt: summary file. </p> <p>Note that some of the rows in Raw data.xls (and in the corresponding "Labelled Data" files) are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.</p>
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Arctic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. W<span>e investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Arctic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. Warm SST and low SIC reshaped time activity budgets (TABs) and diving patterns, causing decreased resting, increased flight, and longer dives. Mercury contamination was not associated with TABs. However, highly contaminated birds lengthened inter-dive breaks when making long dives, suggesting mercury-induced physiological limitations. A</span>s dive durations increased with warm SST<span>, </span>subtle toxicological effects threaten to increasingly constrain diving and foraging efficiency as climate change progresses, with ecosystem-wide repercussions.</p>
Data for LogP toluene/water SAMPL9 challenge
<p>Data for reproducing nonequilibrium fast growth (NE-FG) blind predictions<br> of the toluene/water partition coefficients with the s/w ORAC6.1 <br> (www1.chim.unifi.it/orac) </p> <p>This archive contains the following directories:</p> <p>lib-631Gd -> GAFF2 ORAC top-par with 6-31Gd charges <br> lib-am1bcc -> GAFF2 ORAC top-par with am1bcc charges <br> pdb -> PDB structures of the compounds and solvents <br> work-am1-bcc -> work data with AM1-BCC charge set<br> work-6-31Gd -> work data with HF/6-31Gd charge set<br> bin -> ancillary scripts (see README inside) </p> <p>LogP-analysis -> analysis of all SAMPL9 LogP submissions<br> (see README in the directory for details)<br> </p>
Data and Code for "A planetary health innovation for disease, food, and water challenges in Africa"
<p>Data and R code for both economic and non-economic analysis for the paper titled, "A planetary health innovation for disease, food, and water challenges in Africa"</p>
SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy
<p><strong>DATASET STRUCTURE</strong></p> <p>The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/10.5281/zenodo.7260705</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf".</p> <p>The<strong> training datasets</strong> for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure:</p> <p>Task1.zip/</p> <p>├── Task1</p> <p> ├── brain</p> <p> ├── 1Bxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── ct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_brain_train.xlsx</p> <p> ├── 1Bxxxx_train.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 1Pxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── ct.nii.gz</p> <p> ├── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_pelvis_train.xlsx</p> <p> ├── 1Pxxxx_train.png</p> <p> └── ....</p> <p>Task2.zip/</p> <p>├──Task2</p> <p> ├── brain</p> <p> ├── 2Bxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── ct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 2_brain_train.xlsx</p> <p> ├── 2Bxxxx_train.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 2Pxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── ct.nii.gz</p> <p> ├── mask.nii.gz</p> <p>├── ...</p> <p>└── overview</p> <p> ├── 2_pelvis_train.xlsx</p> <p> ├── 2Pxxxx_train.png</p> <p> └── ....</p> <p>Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:</p> <p>[Task] [Anatomy] [Center] [PatientID]</p> <p>1 B A 001</p> <p>In each patient folder, three files can be found: </p> <ul> <li> <p>ct.nii.gz: CT image </p> </li> <li> <p>mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image</p> </li> <li> <p>mask.nii.gz:image containing a binary mask of the dilated patient outline </p> </li> </ul> <p>For each task and anatomy, an overview folder is provided which contains the following files:</p> <ul> <li> <p>[task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient.</p> </li> <li> <p>[task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.</p> </li> </ul> <p><strong>DATASET DESCRIPTION</strong></p> <p>This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.</p> <p>Data was collected at 3 Dutch university medical centers:</p> <ul> <li> <p>Radboud University Medical Center</p> </li> <li> <p>University Medical Center Utrecht</p> </li> <li> <p>University Medical Center Groningen</p> </li> </ul> <p>For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.</p> <p>The following number of patients is available in the training set.</p> <p><strong>Training</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> <td> <p>120</p> </td> <td> <p>0</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>60</p> </td> <td> <p>180</p> </td> </tr> </tbody> </table> <p>Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets.</p> <p><strong>Validation</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>20</p> </td> <td> <p>0</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p><strong>Testing</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> <td> <p>40</p> </td> <td> <p>0</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>20</p> </td> <td> <p>60</p> </td> </tr> </tbody> </table> <p>In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. <strong>This repository only contains the training data.</strong></p> <p>All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).</p> <p>Data was acquired with the following scanners:</p> <ul> <li> <p>Center A:</p> <ul> <li> <p>MRI: Philips Ingenia 1.5T/3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> <li> <p>Center B:</p> <ul> <li> <p>MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T</p> </li> <li> <p>CT: Siemens SOMATOM Definition AS</p> </li> <li> <p>CBCT: IBA Proteus+ or Elekta XVI</p> </li> </ul> </li> <li> <p>Center C:</p> <ul> <li> <p>MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> </ul> <p>For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.</p> <p>For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT.</p> <p>The following pre-processing steps were performed on the data:</p> <ul> <li> <p>Conversion from dicom to compressed nifti (nii.gz)</p> </li> <li> <p>Rigid registration between CT and MR/CBCT</p> </li> <li> <p>Anonymization (face removal, only for brain patients)</p> </li> <li> <p>Patient outline segmentation (provided as a binary mask)</p> </li> <li> <p>Crop MR/CBCT, CT and mask to remove background and reduce file sizes</p> </li> </ul> <p>The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics.</p> <p><strong>ETHICAL APPROVAL</strong></p> <p>Each institution received ethical approval from their internal review board/Medical Ethical committee:</p> <ul> <li> <p>UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”.</p> </li> <li> <p>UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”.</p> </li> <li> <p>Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”.</p> </li> </ul> <p><strong>CHALLENGE DESIGN</strong></p> <p>The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746020">https://doi.org/10.5281/zenodo.7746020</a>. </p>
ISMRM 2022/23 Reproducibility Team Challenge: Replication results for MReplIcators team
<p>This repository contains the <em><strong>MR</strong></em>epl<em><strong>I</strong></em>cators team’s replication attempt results from the 2022-23 International Society of Magnetic Resonance in Medicine (ISMRM) Challenge ‘Repeat It With Me: Reproducibility Team Challenge’ (<a href="https://challenge.ismrm.org/">ISMRM Challenge – ISMRM's Challenge Forums</a>).</p> <p>The challenge aims to “raise awareness of the importance of reproducible science through fostering collaboration between labs by reproducing ISMRM abstracts”.</p> <p>The Reproducer sub-team was formed by Ebony R. Gunwhy (ERG) and Jemima Pilgrim-Morris (JPM) from POLARIS (<a href="https://www.sheffield.ac.uk/polaris">POLARIS (Pulmonary, Lung and Respiratory Imaging Sheffield) | POLARIS | The University of Sheffield</a>) at The University of Sheffield and the team worked on reproducing abstract #1700 "Sensitivity Analysis of the Bloch Equations" (ISMRM 2022) by Author sub-team Nick Scholand and Martin Uecker from The Institute of Medical Engineering at Graz University of Technology, Austria and Institute for Diagnostic and Interventional Radiology University Medical Center, Göttingen (1).</p> <p>In this archive, a zipped results folder contains the independent results and notes from each member of the replicator sub-team (in ‘results/JPM’ and ‘results/ERG’, respectively). A more detailed overview of the abstract and replication (motivation, approach, challenges, and outcomes) can also be found in ‘Abstract replication details.pdf’. A shorter summary is provided here:</p> <p><em><strong>A successful replication ‘attempt’ was defined as…</strong></em></p> <ul> <li> <p>Installation of provided software</p> </li> <li> <p>Creation of simulations with the BART (2) simulation interface</p> </li> </ul> <p><em><strong>A fully successful replication was defined as…</strong></em></p> <ul> <li> <p>Successful recreation of the figures provided in the abstract</p> </li> <li> <p>Examining goodness-of-fit by matching the provided ground truth dataset to some tolerances (normalised root mean square error; NRMSE)</p> </li> <li> <p>Using quantitative measures of reproducibility (e.g., coefficient of variation) between original data computed by author sub-team and data computed by replicator sub-team</p> </li> <li> <p>Application of technique to further sequences and tissue/sequence parameters</p> </li> </ul> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Contact between Replicator and Author sub-teams</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>An initial meeting was conducted to discuss how we should organise our participation as a team in this challenge. Some starting information was exchanged, in addition to the original abstract:</p> <p><strong>(A)</strong> Publicly published information about the studies:</p> <p>(i) arXiv preprint of more detailed manuscript (2)</p> <p>(ii) GitHub repository associated with the arXiv preprint, containing steps for reproducing the work relating to Figure 2 of the abstract (<a href="https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis">https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis</a>)</p> <p>(iii) GitHub repositories with tutorials on using the BART reconstruction toolbox (<a href="https://github.com/mrirecon/bart-workshop">https://github.com/mrirecon/bart-workshop</a>)</p> <p><strong>(B)</strong> The author sub-team also created an additional GitHub repository made publicly available, containing interactive Google Colab and Binder notebooks introducing BART and its simulation framework, and providing a more step-by-step guide to allow for an exact replication of the work in the abstract (<a href="https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq">https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq</a>). The author sub-team also uploaded the ground truth dataset (their own simulation results, located within ‘02_irbssfp/ref/’ and ‘03_unprep_irbssfp/ref/) from the abstract to this repository.</p> <p>In the first instance, the Replicator sub-team attempted a replication using only the publicly available information outlined in (A). Each member of the Replicator sub-team created their own GitHub forks of the repository listed in (A)(ii) to provide a starting point from which to document their replication from. These can be found at <a href="https://github.com/JemimaPM/bloch-moba-jpm">https://github.com/JemimaPM/bloch-moba-jp</a>m (JPM) and <a href="https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators">https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators</a> (ERG) and contain a commit history of all replication steps taken.</p> <p>The additional repository described in (B) was only used to download the ground truth dataset for quantitatively assessing the replication results. This limitation was imposed to allow the Replicator sub-team autonomy in reproducing the work and to aid in gaining a more thorough understanding of the methodology involved. It was agreed that if required, the replicator sub-team would take help from the extra repository described in (B) and provide feedback to the authors afterwards summarising what degree of information was useful for replicating the work. Further interaction was limited to creating issues on GitHub and to quick queries via email, as needed.</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Expected outcomes</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>The original study was based on numerical experiments. Therefore, the results were expected to be directly reproducible, however it was expected that possible differences may occur due to numerical noise. The major reason for this is the single floating-point arithmetic used in the provided BART software. Its presence can be tested by reproducing the study results on various machines multiple times. Numerical noise is expected to change between different hardware. Therefore, both members of the Replicator sub-team attempted to independently replicate the results on their own hardware. The used hardware in this challenge is specified below.</p> <p><em>Replicators:</em></p> <p>ERG: Intel(R) Core(TM) i5-1145G7 CPU @ 2.60GHz (4 cores, 8 logical processors)</p> <p>JPM: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz (4 cores, 8 logical processors)</p> <p><em>Authors:</em></p> <p>Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz (4 cores, 8 logical processors)</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Replication results</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>Prior to starting the replication attempt, we agreed on the NRMSE tolerances below which we would define the replication as successful. These were NRMSE = 0.005 for the DQ derivative and NRMSE = 0.0001 for the SA derivative.</p> <p>Due to time constraints, only a direct replication was performed. This was deemed successful as both replicators were able to reproduce the results within these tolerances. As expected, differences occurred between hardware, with 0% NMRSE achieved in the replication attempt of JPM. In addition, the reproduced figures were qualitatively compared to the abstract figures and these were found to agree.</p> <p>Further notes and results from the Replicator sub-team attempts can be found within the ‘results’ folder for each respective replictor.</p> <p> </p> <p><strong><em>===========================================</em></strong></p> <p><em><strong>Carbon footprint</strong></em></p> <p>This algorithm runs in 0.08 mins on 4 CPUs and on:</p> <ul> <li> <p>Intel i5-10400F: draws 572.04 Wh. Based in the United Kingdom, this has a carbon footprint of 132.21 g CO2e, which is equivalent to 0.14 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i5-10400F&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i5-10400F (green-algorithms.org)</a>)</p> </li> <li> <p>Intel i7-4930K: draws 1.10 kWh. Based in the United Kingdom, this has a carbon footprint of 253.13 g CO2e, which is equivalent to 0.28 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i7-4930K&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i7-4930K (green-algorithms.org)</a>)</p> </li> </ul> <p>(calculated using green-algorithms.org v2.2 [1]. Note: at time of writing, the Green Algorithms calculator did not include our specific CPUs, therefore here we have calculated with similar processors. A request has been made on the Green Algorithms GitHub repository for our processors to be added in future).</p> <p><em><strong>===========================================</strong></em></p> <p><em><strong>References</strong></em></p> <p><em>1. Scholand N, & Uecker M. Sensitivity Analysis of the Bloch Equations [abstract]. In: Proceedings of the 31st Annual Meeting of ISMRM, London, 2022. Abstract nr 1700.</em></p> <p><em>2. Uecker M, Virtue P , Ong F, Murphy MJ, Alley MT, Vasanawala SS, Lustig M. Software Toolbox and Programming Library for Compressed Sensing and Parallel Imaging [abstract]. ISMRM Workshop on Data Sampling and Image Reconstruction, Sedona, 2013.</em></p> <p><em>3. Scholand N, Wang X, Roeloffs V, Rosenzweig S, Uecker M. Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations. arXiv:2209.08027 [Preprint]. 2022. doi: <a href="https://doi.org/10.48550/arXiv.2209.08027">10.48550/arXiv.2209.08027</a></em></p> <p><em>4. Lannelongue L, Grealey J, Inouye M. Green Algorithms: Quantifying the Carbon Footprint of Computation. Adv. Sci. 2021;8(12):2100707. doi: <a href="https://doi.org/10.1002/advs.202100707">10.1002/advs.202100707</a></em></p> <p> </p> <p> </p>
DENTEX CHALLENGE 2023
<p><strong>DENTEX CHALLENGE</strong></p> <p>We present the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX), organized in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The primary objective of this challenge is to develop algorithms that can accurately detect abnormal teeth with dental enumeration and associated diagnosis. This not only aids in accurate treatment planning but also helps practitioners carry out procedures with a low margin of error.</p> <p>The challenge provides three types of hierarchically annotated data and additional unlabeled X-rays for optional pre-training. The annotation of the data is structured using the Fédération Dentaire Internationale (FDI) system. The first set of data is partially labeled because it only includes quadrant information. The second set of data is also partially labeled but contains additional enumeration information along with the quadrant. The third data is fully labeled because it includes all quadrant-enumeration-diagnosis information for each abnormal tooth, and all participant algorithms will be benchmarked on the third data.</p> <p>DENTEX aims to provide insights into the effectiveness of AI in dental radiology analysis and its potential to improve dental practice by comparing frameworks that simultaneously point out abnormal teeth with dental enumeration and associated diagnosis on panoramic dental X-rays.</p> <p><em>Please visit our website to join <a href="https://dentex.grand-challenge.org/">DENTEX (Dental Enumeration and Diagnosis on Panoramic X- rays Challenge)</a> which is held at MICCAI2023.</em></p> <p> </p> <p><strong>DATA</strong></p> <p>The DENTEX dataset comprises panoramic dental X-rays obtained from three different institutions using standard clinical conditions but varying equipment and imaging protocols, resulting in diverse image quality reflecting heterogeneous clinical practice. The dataset includes X-rays from patients aged 12 and above, randomly selected from the hospital's database to ensure patient privacy and confidentiality.</p> <p>To enable effective use of the FDI system, the dataset is hierarchically organized into three types of data;</p> <ul> <li> <p>(a) 693 X-rays labeled for quadrant detection and quadrant classes only,</p> </li> <li> <p>(b) 634 X-rays labeled for tooth detection with quadrant and tooth enumeration classes,</p> </li> <li> <p>(c) 1005 X-rays fully labeled for abnormal tooth detection with quadrant, tooth enumeration, and diagnosis classes.</p> </li> </ul> <p>The diagnosis class includes four specific categories: caries, deep caries, periapical lesions, and impacted teeth. An additional 1571 unlabeled X-rays are provided for pre-training.</p> <ul> <li>The dataset is used for <a href="https://dentex.grand-challenge.org/">DENTEX (Dental Enumeration and Diagnosis on Panoramic X-rays Challenge)</a>.</li> </ul> <p><strong>Data Split for Evaluation and Training</strong></p> <p>The DENTEX 2023 dataset comprises three types of data: (a) partially annotated quadrant data, (b) partially annotated quadrant-enumeration data, and (c) fully annotated quadrant-enumeration-diagnosis data. The first two types of data are intended for training and development purposes, while the third type is used for training and evaluations.</p> <p>To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth, and the testing data is kept hidden from participants.</p> <p><strong>Annotation Protocol</strong></p> <p>The DENTEX provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. Although it may seem redundant to provide a quadrant detection dataset, it is crucial for utilizing the FDI Numbering System. The FDI system is a globally-used system that assigns each quadrant of the mouth a number from 1 through 4. The top right is 1, the top left is 2, the bottom left is 3, and the bottom right is 4. Then each of the eight teeth and each molar are numbered 1 through 8. The 1 starts at the front middle tooth, and the numbers rise the farther back we go. So for example, the back tooth on the lower left side would be 48 according to FDI notation, which means quadrant 4, number 8. Therefore, the quadrant segmentation dataset can significantly simplify the dental enumeration task, even though evaluations will be made only on the fully annotated third data.</p> <p>Note: The datasets are fully identical to the data used for our baseline method named as HierarchicalDet. Therefore, please visit <a href="https://github.com/ibrahimethemhamamci/HierarchicalDet">HierarchicalDet (Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays)</a> repo for more info.</p> <p> </p> <p><strong>CITING US</strong></p> <p>If you use DENTEX, we would appreciate references to the following papers.</p> <ol> <li>Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze., Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays, 2023.<br> Pre-print: <a href="https://arxiv.org/abs/2303.06500">https://arxiv.org/abs/2303.06500</a></li> <li> <p>Hamamci, I., Er, S., Simsar, E., Yuksel, A., Gultekin, S., Ozdemir, S., Yang, K., Li, H., Pati, S., Stadlinger, B., & others (2023). DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays<em>.</em><em> </em><br> Pre-print: <a href="https://arxiv.org/abs/2305.19112">https://arxiv.org/abs/2305.19112</a></p> </li> </ol>
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.