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2,863 results for “Challenge”
Wide-Area Geolocalization with a Limited Field of View Camera in Challenging Urban Environments Dataset
<p>Rectified imagery with latitude and longitude tags for paths driven throughout Cambridge and Boston, MA in November 2022. Faces and license plates have been blurred for anonymity. Dataset created for “Wide-Area Geolocalization with a Limited Field of View Camera in Challenging Urban Environments” paper, currently under review in IEEE Transactions on Robotics. Lena M. Downes is a Draper Scholar at MIT.</p>
Small Modular Reactor Neutronics Challenge Problem Input Files for OpenMC
<p>OpenMC is part of the small modular reactor (SMR) simulation subproject "ExaSMR" in the broader Exascale Computing Project (ECP), which is a US DOE project. The goal of the ExaSMR project is to enable extreme-fidelity simulation of SMRs, which have been a recent focus of engineering development. These reactors are of great interest due to their ability to economically provide consistent baseload power while avoiding many of the downsides of traditional (large) reactor designs.</p> <p>Part of the ExaSMR project was to define a representative challenge problem. The problem defined in this input dataset to OpenMC is a realistic SMR geometry that represents the NuScale reactor design recently licensed by the US Nuclear Regulatory Commission. The SMR problem is also defined with the goal of depletion calculations in mind, where hundreds of nuclides are present in the reactor fuel, and isoptic content lists are maintained for hundreds of thousands of unique fuel burnup regions throughout the reactor core. While the SMR problem is defined in-depth in Reference [1], we give an overview of additional key input parameters below that illustrate the difficulty of the simulation:<br> </p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> </tr> </thead> <tbody> <tr> <td>Number of unique materials</td> <td>195,368</td> </tr> <tr> <td>Number of unique fuel materials</td> <td>195,360</td> </tr> <tr> <td>Number of depletion tally regions</td> <td>195,360</td> </tr> <tr> <td>Number of nuclides in each fuel region</td> <td>244</td> </tr> <tr> <td>Number of nuclides in simulation</td> <td>281</td> </tr> </tbody> </table> <p>Overall, the ExaSMR challenge problem forms an ideal basis for performance analysis due to its relevance to the nuclear industry and its prominence as a high performance computing "hero problem" as part of ECP. For these reasons, we will use the SMR problem as the main basis of our performance analysis and scaling challenges.</p> <p>The model is also available in python format at: <a href="https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model">https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model</a></p> <p>References:</p> <p>[1] Kord Smith. 2017. NuScale Small Modular Reactor (SMR) Progression Problems for the ExaSMR Project. Milestone Report WBS 1.2.1.08 ECP-SE-08-43. Exascale Computing Project.</p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
MICCAI FLARE22 Challenge Dataset (50 Labeled Abdomen CT Scans)
<p>This dataset was used as the labeled training set in MICCAI FLARE 2022 Challenge https://flare22.grand-challenge.org/.</p> <p>The CT images and pancreas annotations are from http://medicaldecathlon.com/</p> <p>The other organ annotations are from AbdomenCT-1K (research purpose only).</p> <p>If this dataset is useful in your research, please give credit to the following two papers:</p> <p>A large annotated medical image dataset for the development and evaluation of segmentation algorithms</p> <p>https://arxiv.org/abs/1902.09063</p> <p>AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?</p> <p>https://ieeexplore.ieee.org/document/9497733</p>
Data from: Immune challenge reduces daily activity period in free-living birds for three weeks
<p>Non-lethal infections are common in free-living animals and the associated sickness behaviours can impact crucial life-history trade-offs. However, little is known about the duration and extent of such sickness behaviours in free-living animals, and consequently how this affects life-history decisions. Here, free-living Eurasian blackbirds <em>Turdus merula</em> were immune-challenged with lipopolysaccharide (LPS) to mimic a bacterial infection, and their behaviour was monitored for up to 48 days using accelerometers. As expected, immune-challenged birds were less active than controls within the first 24 hours. Unexpectedly, this reduced activity remained detectable for 20 days, before both groups returned to similar activity levels. Furthermore, activity was positively correlated with a pre-experimental index of complement activity, but only in immune-challenged birds, suggesting that sickness behaviors are modulated by constitutive immune function. Differences in daily activity levels stemmed from immune-challenged birds resting earlier at dusk than control birds, while activity levels between groups were similar during core daytime hours. Overall, activity was reduced by 19% in immune-challenged birds and they were on average almost 1 hour less active per day for 20 days. This unexpected longevity in sickness behaviour may have severe implications during energy-intense annual-cycle stages (e.g., breeding, migration, winter). Thus, our data help to understand the consequences of non-lethal infections on free-living animals.</p>
Learn2Reg Challenge: CT Lung Registration
<p>learn2reg_lungData_OriginalSize.zip contains the images used in the Learn2Reg challenge in the Lung CT task in the original resolution.</p> <p>For more information about this dataset go to: https://learn2reg.grand-challenge.org/</p> <p> </p>
Stakeholder views on the barriers and facilitators of psychosocial interventions to address reduction in aggressive challenging behaviour in adults with intellectual disabilities
<p>Success of psychosocial interventions in reducing aggressive challenging behaviour is likely to be related not only to mechanistic aspects but also to therapeutic and system factors. The study aims to examine the facilitators and barriers that influence whether psychosocial interventions for aggressive challenging behaviour in adults with intellectual disabilities lead to positive change. We conducted 42 semi-structured interviews with adults with intellectual disabilities and aggressive challenging behaviour, family/paid carers, and professionals engaged in or delivering a psychosocial intervention across the UK. Data were analysed thematically using a framework approach. Stakeholders considered therapeutic and supportive relationships and personalised care as facilitating factors of psychosocial interventions to address aggressive challenging behaviour. The operational structure of community intellectual disability services and conflicting expectations of professionals and carers were the main contextual barriers that impeded the implementation of psychosocial interventions addressing aggressive challenging behaviour in adults with intellectual disabilities. Findings highlight the valued components that maximise positive change in adults with intellectual disabilities who display aggressive challenging behaviour. Several operational adjustments including referral criteria, roles of professionals and workforce issues need to be addressed in services to maximise the implementation of psychosocial interventions to reduce aggressive challenging behaviour in adults with intellectual disabilities.</p>
Net-zero transition of the global chemical industry with CO2-feedstock by 2050: feasible yet challenging
<p>This dataset contains supplementary data and code for the following publication:</p> <p>Title: Net-zero transition of the global chemical industry with CO2-feedstock by 2050: feasible yet challenging<br> Authors: Jing Huo, Zhanyun Wang, Christopher Oberschelp, Gonzalo Guillén-Gosálbez and Stefanie Hellweg<br> Year: 2023<br> Journal: Green Chemistry<br> Issue: 25<br> Pages: 415-430<br> Publisher: Royal Society of Chemistry<br> Direct link: https://doi.org/10.1039/D2GC03047K</p> <p>Please read "read_me.txt" for more details.</p>
Amr data challenge
<p>Graphs and plots </p>
Localizing Commitments, Challenges, and Insights on the Road to Immunization Agenda 2030: Responses from 6,185 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)
<p># Localizing Commitments, Challenges, and Insights on the Road to Immunization Agenda 2030: Responses from 6,185 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)</p> <p>## Abstract<br> This data set contains survey responses from over 6,000 national and subnational immunization staff who applied to participate in a 2022 Full Learning Cycle (FLC) learning programme of The Geneva Learning Foundation (TGLF), intended to contribute to the Movement for Immunization Agenda 2030. The 95-item questionnaire collected information on respondents' commitment to the movement's principles, demographics, work challenges, motivation and learning culture, and the impact of COVID-19 on routine immunization. The purpose was to understand applicants' priority challenges and readiness to engage in peer learning to advance country and global immunization goals. Questions addressed consent, identity confirmation, COVID-19 vaccination status, employer, role, system level, past participation in the sponsoring organization's programs, work and wellbeing, difficulties with COVID-19 vaccination, outbreak response, gender equity, and reaching zero-dose children. Applicants identified one priority challenge in their work that they would seek to address through the program. This data set offers insights into frontline perspectives on strengthening immunization programs. Secondary analysess were performed in 2022 and 2023 to illuminate human resource issues, gender barriers, pandemic recovery, and peer learning for change.</p> <p>## Research Audience</p> <p>Education and global health researchers with interest in human resources for health (HRH) and the characteristics, priority challenges, and experiences of national and sub-national immunization staff participating in the Movement for Immunization Agenda (IA) 2030.</p> <p>## Credits</p> <p>### Author</p> <p>The Geneva Learning Foundation (TGLF)<br> 18 Avenue Louis Casaï <br> CH1209 Geneva, Switzerland<br> research@learning.foundation</p> <p>### Principal Investigator and Corresponding Author<br> Reda Sadki, TGLF<br> reda@learning.foundation</p> <p>### Project Partners<br> - Biostat Global Consulting (BGC)<br> - Bridges to Development<br> - Centre for Change & Complexity in Learning (C3L)</p> <p>### Partners' Roles and Responsibilities<br> - Design: TGLF<br> - Implementation (sample collection): TGLF<br> - Processing: TGLF, C3L<br> - Anonymization: BGC<br> - Data cleaning: Bridges to Development<br> - Submission: TGLF<br> - Maintenance of learning analytics database where data are stored: C3L</p> <p>### Funding<br> Wellcome Trust, Bill & Melinda Gates Foundation (BMGF)</p> <p>## Recommended Citation<br> The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation. [DOI:https://doi.org/10.5281/zenodo.8199552](https://zenodo.org/record/8199552)</p> <p>## File List<br> IA2030_EN_FLC_2022_Application_Survey.README.md (this document)</p> <p>20220211.IA2030-EN Movement application-FINAL.docx: List of questions included in the questionnaire. (Note: skip patterns are not shown.)</p> <p>IA2030_EN_Application_Survey_Dataset.xlsx: English version of anonymized Application Survey Dataset. Version 1: Geneva Learning Foundation, 11 August 2023. (6,669 observations; 58 variables)</p> <p>## Related Data Sets<br> This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the first IA2030 Full Learning Cycle (FLC). The complete IA2030 Application Survey data set is more comprehensive, and includes information such as respondents' gender, employer, professional role, country, and health system level, as well as responses to open-text questions. </p> <p>Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a Data Sharing Agreement that would stipulate conditions of access (insights@learning.foundation).</p> <p>The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: https://doi.org/10.5281/zenodo.7763922 </p> <p>Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from TGLF's Insights Unit (<insights@learning.foundation>).</p> <p>## Survey Purpose</p> <p>The Immunization Agenda 2030, the global immunization strategy for 2021-2030, set ambitious targets for global immunization coverage and other key indicators (World Health Organization [WHO], 2023a).</p> <p>In response to the WHO Director-General's call for a social movement to ensure immunization remains a priority for global and regional health agendas and promote broad societal support for immunization (WHO, 2023b), TGLF, working with its global community of over 35,000 alumni, developed a learning programme intended to contribute to a “Movement for Immunization Agenda 2030 (IA2030)”.</p> <p>In addition to participating in structured peer learning activities, applicants made a pledge to work towards IA2030 and their country's goals, adhere to the IA2030 core principles, and to provide support to their peers making similar commitments.</p> <p>The purpose of the IA2030 Application Survey was to collect demographic and organizational information from immunization workers who work at the national or sub-national level interested in applying for the 2022 Learning Cycle and for membership in the Movement for IA2030.</p> <p>## Survey Questionnaire<br> The survey questionnaire consisted of both quantitative (Likert) and qualitative (open-text) responses to 95 questions documenting respondents' commitment to joining the Movement for IA2030 and adhering to relevant principles, demographic characteristics, information about work history and role, work and well-being, learning culture and performance, COVID-19 recovery efforts through vaccination campaigns and routine immunization (including outreach), priority work-related challenges, and most important reason for wanting to join the Movement for IA2030.</p> <p>Survey content was informed by TGLF's six years of experience working with thousands of immunization workers from over 90 countries.</p> <p>The survey was administered in English and French. While most of questions were required, several items, including questions about work and well-being and COVID-19 vaccination status, were optional.</p> <p>### Question Scaling and Response Options<br> Most questions were asked with a 'select one response' instruction, but several encouraged the respondent to 'select all that apply'.<br> - AP_CAR_20 Which of these job categories apply to you?<br> - AP_ENV_55 Where you work, what strategies have been put in place to reach under immunized or zero-dose children?<br> - AP_CHA_63 Is your challenge related to any of these?<br> - AP_ENV_78 What actions are being taken at your level of the health system to strengthen RI or PHC that specifically takes advantage of some aspect of COVID-19 vaccine introduction?<br> - AP_ENV_87 Select all activities used for catch-up.<br> - AP_ENV_91 What were the disruptions related to?</p> <p>Each person's several responses are stored in a single text variable and separated by commas. Some data management will be necessary to divide these strings of text into individual variables to represent each response option.</p> <p>## Overview of questionnaire for respondents<br> The following information was shared with all applicants to provide an overview of the questions and their rationale.</p> <p>First, we ask you for:<br> - Consent to share your data, to confirm your supervisor’s support, and to make commitments to follow country and WHO guidelines on COVID-19 and immunization<br> - Your legal name and birthdate to confirm your identity for certification.<br> - Your WhatsApp number to connect you with other participants in the Movement.<br> - Your organization, role, and health system level, and if you are a TGLF alum.<br> - We ask you about your work and well-being: Before we ask you about the challenges you face, we ask about your work and well-being, especially your motivation and how learning is being supported where you work.<br> - We ask about the challenges you face: In 2020, global immunization coverage levels for infants dropped back to 2009 levels. It is like we lost 11 years of hard work. So we ask you about the challenges you face: COVID-19 vaccination, epidemic outbreaks (measles, yellow fever, etc.), gender barriers, and zero-dose children.<br> - We ask you to pick the challenge that you will work on in the Movement: Then we ask you to identify your most difficult and important challenge. This is the one that you will focus on in the Movement. (You can always change later.)<br> - Are you truly committed to learning with colleagues from all over the world? Because the Movement is about learning, sharing experience, and collaborating with others, we ask you to confirm to what extent this is what you want to do.<br> - We ask you to share your successes, ideas, and lessons learned: Because we know that you have many strengths, we ask you if you want to share a success story, and idea, or a lesson learned with colleagues.<br> - We ask you if you want to help build and shape the Movement for IA2030: We ask you if you want to join the Organizing Committee to help build the Movement for Immunization Agenda 2030.<br> - Global partners request your help: Finally, we ask you to answer questions that IA2030 global partners are specifically interested in, about the effect of COVID-19 on routine immunization, catch-up activities, and your own COVID-19 vaccination. (You can choose to skip these questions.)</p> <p>### Specific questions that respondents were encouraged to reflect upon before writing out their answers<br> - Tell us more about your work and well-being. What are the worst and best parts of your job? Where do you find the motivation to continue your work? What helps you feel involved in your work? How has the COVID-19 pandemic changed how you feel about your work? What makes you lose or gain your motivation? <br> - Tell us more about the effect of the introduction of the COVID-19 vaccine for routine immunization (RI) and primary health care (PHC) services in the area where you work. Explain what has happened. What measures, if any, have been taken to mitigate disruptions or potential disruptions to health workers involvement in routine immunization due to COVID-19 vaccination activities where you work?<br> - Do you have a story about gender barriers to immunization that you would like to share?<br> - Tell us more about what you are doing to reach zero-dose children and missed communities. <br> - Tell us more about how we can better support you. What are the barriers for you to tackle the challenge you face? For your challenge, what kind of support would be most helpful to you? Why? Tell us more about your motivation to join the Movement for Immunization Agenda 2030. What do you hope to gain? What do you want to contribute? How will participating in this Movement help you improve your work?<br> - Do you have something that you want to share with colleagues? What you share does not need to be a “big” idea. We are interested in what you are doing that helps you to continue or improve your immunization work, even in a small way.</p> <p>## Ethical Considerations<br> Prior to fielding of the survey, the survey instrument and protocol were subjected to ethical and legal review by TGLF.</p> <p>Prior to beginning the survey, prospective respondents reviewed a User Agreement and a summary of TGLF's Privacy Policy (https://www.learning.foundation/pages/privacy), which provides information about the personal information the Foundation collects; the ways in which the Foundation uses personal information; and TGLF's procedures for protecting the loss, misuse or alteration of personal information.</p> <p>Participants who chose to respond to the items relevant to work and well-being and organizational culture were promised that their responses would be strictly confidential and that only anonymous, aggregated data would be shared with others.</p> <p>## Recruitment Process and Description of the Sample<br> Members of TGLF's immunization learning community were invited to participate in the survey by email and were encouraged to invite relevant colleagues (immunization workers who work at either the national or sub-national level) to participate by sharing the invitation.</p> <p>Respondents accessed the survey via the Internet by clicking on the survey link contained in the email.</p> <p>Inclusion criteria for participation in the survey included being an immunization worker who works at the national or sub-national level.</p> <p>Only individuals who indicated their direct supervisor at work agreed with their participation and who completed the consent form were permitted to complete a survey.</p> <p>## Survey Design and Administration<br> This one-time Internet-based survey was administered as part of the launch of the IA2030 Movement.</p> <p>The survey was available from February 22, 2022 - June 17, 2022.</p> <p>Respondents did not receive any incentives for completing the survey.</p> <p>The survey was hosted on the Typeform server. The survey itself and all information collected for the survey was secured with multiple levels of encryption and access controls to guarantee participant confidentiality, per Typeform policy (https://www.typeform.com/help/a/security-at-typeform-360029259552/?tid=200afff9-65b2-46e2-9553-0063f2c808b6).</p> <p>## Data Processing and Cleaning<br> Duplicate surveys were identified using respondents' email addresses and were removed from the data file.</p> <p>When a duplicate survey was submitted, only the most recent survey data were retained.</p> <p>## Data Privacy and Anonymization<br> The data file shared on this site is intentionally incomplete. Columns holding personally identifying information (e.g., email address, gender, country) have been deleted.</p> <p>Out of an abundance of caution, columns that could potentially hold personally identifying information (i.e., responses to open-text questions) have been deleted.</p> <p>In accordance with the privacy policy noted in the questionnaire, responses to questions related to work and well-being and organizational culture have also been redacted.</p> <p>A complete list of the variables that have been redacted from the data file is provided in the data file "Summary" tab.</p> <p>Each respondent's record holds a unique identifier in the column named AP_DAT_99.</p> <p>## Data Availability and Accessibility<br> This data set is being made available through Zenodo, an open access repository. There are no restrictions on access to open data except those stated in the copyright and license. Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a Data Sharing Agreement that would stipulate conditions of access (insights@learning.foundation).</p> <p>## Copyright and License<br> © The Geneva Learning Foundation 2022.</p> <p>This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</p> <p>Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-nc-sa/4.0/.</p> <p>Under the terms of this license, you may copy, redistribute and adapt the data set for non-commercial purposes, provided the work is appropriately cited, as indicated above. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products, or services. Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization.</p> <p>General disclaimers. The designations employed and the presentation of the data set do not imply the expression of any opinion whatsoever on the part of the Foundation concerning the legal status of any country, territory, city or area or of its authorities. The mention of specific companies or of certain manufacturers' products does not imply that they are endorsed or recommended by the Foundation in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of all reasonable precautions have been taken by the Foundation to verify the information contained in this data set. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use.</p> <p>The data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation.</p> <p>Version 1.0 (11 August 2023).</p> <p>## References<br> Asiwome Kosi Abiwu, P.H., and Jones, I. (2022). IA2030 Case study 5. Paul Hilarius Asiwome Kosi Abiwu. View from the frontline: building stronger systems (The Geneva Learning Foundation) 10.5281/ZENODO.7004284.</p> <p>Ateke, N.A., and Jones, I. (2022). IA2030 Case study 4. Njoh Andreas Ateke. View from the frontline: working with communities to strengthen immunization programmes (The Geneva Learning Foundation) 10.5281/ZENODO.7004263.</p> <p>Ekwam, D.K., and Jones, I. (2022). IA2030 Case study 13. Pharm. Daniel Kwesi Ekwam. Covering all the bases (The Geneva Learning Foundation) 10.5281/ZENODO.7010227.</p> <p>Faye, W., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 18. Wasnam Faye. Vaccine angels - Give us the opportunity and we can perform miracles. (The Geneva Learning Foundation) 10.5281/ZENODO.7785244.</p> <p>Faye, W., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 18. Wasnam Faye. Les anges de la vaccination : « Donnez-nous l'occasion et nous ferons des miracles. » (The Geneva Learning Foundation) 10.5281/ZENODO.7849329.</p> <p>Ferrah Conteh, Z., and Jones, I. (2022). IA2030 Case study 6. Zainab Ferrah Conteh. View from the frontline: Strenghtening the immunization programme backbone (The Geneva Learning Foundation) 10.5281/ZENODO.7010079.</p> <p>Iwot, N., and Jones, I. (2022). IA2030 Case study 14. Ndaeyo Iwot. Building the capacities of communities (The Geneva Learning Foundation) 10.5281/ZENODO.7010211.</p> <p>Jones, I., Mbuh, C., and Sadki, R. (2023). Listening and Learning at Teach to Reach 7 (IA2030 Listening and Learning Report 4) (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7766585.</p> <p>Jones, I., Mbuh, C., and Sadki, R. (2023). Teach to Reach 7: Partager pour apprendre (Rapport IA2030 «Écouter pour apprendre n° 4) (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7851783.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 1. A digital health platform for immunization and primary health care (PHC) engagement (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.6982953.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 1. A digital health platform for immunization and primary health care (PHC) engagement (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.6982953.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case Study 10. Development of zero dose situational analyses and action plans (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010177.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 2. Squaring the circle: safeguarding existing immunization programmes and primary healthcare during COVID-19 vaccination (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004191.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case Study 8. Practitioners' perceptions of key immunization challenges (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7005241.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Listening and learning report 1. Working groups consultative engagement through a peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010140.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Movement Year 1 report. Consultative engagement through a digitally enabled peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7119648.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030. Listening and learning report 3. Everyone, everywhere: digital peer learning to leave no one behind (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010209.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). Mouvement pour la vaccination à l'horizon 2030. Rapport Écouter pour apprendre n° 3. Partout, pour tous: l'apprentissage numérique par les pairs pour que personne ne reste sur le côté (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7142102.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., Kovanovic, V., et al. (2022). IA2030 Rapport «Écouter pour apprendre» n° 2. Notre engagement pour la vaccination #VaccinesWork (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7254160.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., Kovanovic, V., et al. (2022). IA2030.Listening and learning report 2. It takes people to make #VaccinesWork (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010196.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., et al. (2022). IA2030 Case Study 9. Understanding and responding to zero-dose challenges (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010171.</p> <p>Jones, I., Watkins, K.E., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., et al. (2022). IA2030 Case Study 7. Motivation, learning culture and programme performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004304.</p> <p>Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V.(2022). IA2030 Year 1 report. Consultative engagement through a digitally enabled peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7119648.</p> <p>Jones, I., Watkins, K.E., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., et al. (2022). IA2030 Case Study 7. Motivation, learning culture and programme performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004304.</p> <p>Kaboré, B., and Jones, I. (2022). IA2030 Case study 12. Boureima Kaboré. Delivering services in conflict-affected areas (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010188.</p> <p>Kuotong Nongho, R., and Jones, I. (2022). IA2030 Case study 3. Kuotong Nongho Rogers. View from the frontline: microplanning for equity (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004235.</p> <p>Mbuh, C., Gasse, F., Jones, I., Sadki, R., Brooks, A., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 16. Continuum from knowledge to performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7014392.</p> <p>Monga, F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 19. Franck Monga. Building a movement for IA2030 in the Democratic Republic of Congo (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7794726.</p> <p>Monga, F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 19. Franck Monga. Construire un Mouvement pour la vaccination en République Démocratique du Congo (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7851476.</p> <p>Monzón, M.F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 17. María Fernanda Monzón. Speaking up for frontline staff (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7785024.</p> <p>Monzón, M.F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 17. María Fernanda Monzón. Prendre la parole au nom des personnels de première ligne (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7848308.</p> <p>Oketch, P., and Jones, I. (2022). IA2030 Case study 15. Penina Oketch. Planning, people and performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010242.</p> <p>Sadki, R., Mbuh, C., Szylovec, A.P., and Brooks, A. (2023). Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022). https://doi.org/10.5281/zenodo.7763922.</p> <p>WHO (2020). Immunization Agenda 2030: A Global Strategy to Leave No One Behind [Internet]. Available from: https://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030. </p> <p>Yagnik, A., Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., et al. (2022). IA2030 Case Study 11. Gender barriers to immunization (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010184.</p>
Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
<p class="MsoBodyText"><span>Beluga (<em>Delphinapterus leucas</em>) from the St. Lawrence Estuary, Canada, have been declining since the early 2000s, suggesting recruitment issues as a result of low fecundity, abnormal abortion rates or poor calf or juvenile survival. Pregnancy is difficult to observe in cetaceans, making the ground-truthing of pregnancy estimates in wild individuals challenging. Blubber progesterone concentrations were contrasted among 62 SLE beluga with a known reproductive state (i.e., pregnant, resting, parturient, and lactating females), that were found dead in 1997–2019. The suitability of a threshold obtained from decaying carcasses to assess reproductive state and pregnancy rate of freshly-dead or free-ranging and blindly-sampled beluga was examined using three statistical approaches and two datasets (135 freshly-harvested carcasses in Nunavik, and 65 biopsy-sampled SLE beluga). Progesterone concentrations in decaying carcasses were considerably higher in known-pregnant (mean </span><span>±</span><span> sd: </span><span>365 </span><span>±</span><span> 244 ng g<sup>-1</sup> of tissue) </span><span>than resting (</span><span>3.1 </span><span>±</span><span> 4.5 ng g<sup>-1</sup> of tissue</span><span>) or lactating (</span><span>38.4 </span><span>±</span><span> 100 ng g<sup>-1</sup> of tissue</span><span>) females. An approach based on statistical mixtures of distributions and a logistic regression was compared to the commonly-used, fixed threshold approach (here, 100 ng g<sup>-1</sup>) for discriminating pregnant from non-pregnant females. The error rate for classifying individuals of known reproductive status was the lowest for the fixed threshold and logistic regression approaches, but the mixture approach required limited <em>a priori</em> knowledge for clustering individuals of unknown pregnancy status. Mismatches in assignations occurred at lipid content <10% of sample weight. Our results emphasize the importance of reporting lipid contents and progesterone concentrations in both units (ng g<sup>-1 </sup>of tissue and ng g<sup>-1</sup> of lipid) when sample mass is low. By highlighting ways to circumvent potential biases in field sampling associated with capturability of different segments of a population, this study also enhances the usefulness of the technique for estimating pregnancy rate of free-ranging population. </span></p>
Dataset to paper "ECN with QUIC: Challenges in the Wild"
<p>In the following you can find the according measurements of our IMC'23 paper "ECN with QUIC: Challenges in the Wild" for the publicly available toplists. Please note that we cannot publicly share our CZDS-based measurements due to their proprietary license.</p> <p>All data is available as line-delimited json files which have been zstandard compressed.</p> <p>If you use any portion of our work, please consider citing our publication.</p> <pre><code>@Inproceedings { 2023-sander-quic-ecn, title = {ECN with QUIC: Challenges in the Wild}, year = {2023}, month = {10}, publisher = {ACM}, booktitle = {Proceedings of the Internet Measurement Conference (IMC '23)}, DOI = {10.1145/3618257.3624821}, author = {Sander, Constantin and Kunze, Ike and Bl{\"o}cher, Leo and Kosek, Mike and Wehrle, Klaus} }</code></pre>
The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges
<p>Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"</p>
Hyper PUF with multi-level operation: Challenge-Response Pair Library
<p>An optical PUF with multi level operation (that we named as Hyper PUF) is interrogated with a set of 100 Random Probing Challenges producing in the far field an optical response, the speckle pattern.</p> <p>The multi-level operation is induced by modifying the scattering properties of the PUF with a second light pattern, the Transforming Level.</p> <p>Per each Transforming Level, we acquired the Responses to the 100 Random Probing Challenges.</p> <p>In this repository, we collect the Random Probing Challenges, the ten Transforming Levels and the relative Responses to the Probing Challenges per each Transforming Level.</p>
Immune challenge changes social behaviour in the Guppy (Poecilia reticulata)
<p>Increased exposure to pathogens is often considered to be one of the most significant costs of group living. As a result, animals typically avoid close association with individuals that manifest symptoms of disease. The question remains, however, whether avoidance behaviours are mediated by effects relating specifically to the disease itself, or through recognition of more general sickness behaviours that emerge from the activity of the immune system in response to infection. Here we examined the effects of an immune challenge, induced through exposure to bacterial lipopolysaccharides (LPS), on individual and social behaviour in female guppies (<em>Poecilia reticulata</em>). We found that in a binary choice presentation, healthy focal individuals showed a strong preference to associate with stimulus shoals of saline-injected fish rather than stimulus shoals of LPS-injected fish. In a subsequent examination of group-level dynamics, shoals of LPS-injected females were more dispersed than similar shoals comprised of control fish, and showed less coherent collective behaviour despite the lack of any obvious difference in general activity between LPS and control fish. We discuss these findings in relation to the need for social animals to mitigate the risk of disease that is associated with living in proximity to conspecifics.</p>
The IDEA Challenge 2022 dataset
<p>The IDEA Challenge 2022 prototyping dataset contains a total of 240 prototype entries with 1049 edges (connections) and can provide valuable insights into prototyping practices, offering practical knowledge for developing prototyping strategies and generating hypotheses for future studies. The data were collected using Pro2booth - an online platform which captured comprehensive information about prototypes and participating teams’ development process, including details about the creators, purpose, timing, and method of creation. It is particularly relevant to design researchers, engineering and design students, educators, and industry professionals seeking to enhance their prototyping skills and strategies. The dataset serves as a foundation for subsequent studies, allowing for comparative analyses, hypothesis verification, and trend exploration. It also has the potential to inform meta-analyses across similar design scenarios, providing a comprehensive understanding of prototyping processes.</p> <p> </p>
Transit agencies want open payments but there are challenges
<p>This study explores interest in, and the challenges faced by transit agencies and operators in the adoption of open-loop payment systems. The research team focuses in particular on the ways that agencies view passenger needs in the context of adopting open payments. Challenges with cash payments, an increasingly cashless society, and the expanding offerings of digital payment options have spurred increased interest in open-loop payments among transit operators. Paying for transit with cash can require additional time at boarding, add extra steps for passengers who must pay with exact fare, and result in service inefficiencies. It presents security concerns for drivers, and administrative burdens for agencies. While the full costs of cash handling vary per agency the cost of handling and moving cash may be considerable. Pioneering transit agencies are adopting open payment systems that accept credit cards, debit cards, and smartphone/watch-based transactions. However, there is a huge diversity among transit agencies and as such, agencies face different challenges and to different degrees, when considering the adoption of open payment systems. Challenges can include financial barriers, capacity limitations, technological challenges, the duration of existing contracts, competing needs, and a number of passenger challenges such as lack of credit cards or smartphones. This study uses data collected from California transit agencies in the fall of 2022 that gathered information about agency perceptions of open-loop payments and the challenges with adopting open fare collection systems, and whether assistance programs would benefit transit agencies interested in adopting open-loop payments. Even before the pandemic prompted agencies to consider open and contactless payments, many agencies were exploring this option. Results of the present study indicate that the majority of agencies are considering or have considered implementing open payment systems, but agencies are not fully aware of the assistance available from the California Integrated Travel Program to help in the transition to digital and open payment systems. Key challenges include the cost, as well as the technical needs to implement open payments. Agencies also cited passenger familiarity with technology and ability to have a bank account as potential hurdles. This study sheds light on the challenges facing small to medium transit agencies in the transition of California's transit systems to open-loop payment systems.</p>
Results of the ISMRM 2020 joint Reproducible Research & Quantitative MR study groups reproducibility challenge on phantom and human brain T<sub>1</sub> mapping
<p>This dataset includes both raw data and processed T1 maps obtained from the 2020 challenge on inversion recovery T1 mapping organized by the International Society in Magnetic Resonance in Medicine (ISMRM) Reproducible Research Study Group (RRSG). For a comprehensive overview of the data distribution submitted for this challenge, please visit <a href="https://rrsg2020.db.neurolibre.org/">https://rrsg2020.db.neurolibre.org</a>. It's important to note that this dataset exclusively comprises ISMRM-NIST system phantom data.</p> <p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/rrsg2020/paper NeuroLibre fork:https://github.com/roboneurolibre/paper</p> <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/14">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org">https://neurolibre.org</a></strong></p>
How Do ML Practitioners Perceive Explainability? An Interview Study of Practices and Challenges
<p>This repository contains supplementary materials for our interview Study.</p>
Sketchfab 3D Challenge: Mystreet3Dscan Mailbox
This antique mailbox is currently not in use anymore but is still preserved in the streets of the old beguinage in Lier, Belgium. Photogrammetry model, pictures taken on two different days with Nikon D5300 and AF-S NIKKOR 35mm 1:1.8G lens, images enhanced in DxO, geometry reconstructed in PhotoScan, decimated and cleaned in ZBrush, textured in PhotoScan, platform built in Rhino. Source: Objaverse 1.0 / Sketchfab
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.