chore(openchallenges): 2024-11-27 DB update (#2911)

Co-authored-by: vpchung <[email protected]>

apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv

@@ -520,4 +520,11 @@
 "519","chaimeleon-re-identification-challenge","Chaimeleon Re-identification Challenge","Re-identify pseudonymized medical studies","Participants will be challenged to re-identify pseudonymized medical studies with two de-identification methods used in ongoing AI4HI projects.","","https://chaimeleon.eu/re-identification-challenge/","completed","\N","","2024-05-01","2024-08-01","2869","2024-05-20 16:52:45","2024-07-19 17:06:44"
 "520","digitally-derived-endpoints-for-freezing-of-gait-detection-defogd-challenge","Digitally-Derived Endpoints for Freezing-of-Gait Detection (DEFoGD) Challenge","Digitally-Derived Endpoints for Freezing-of-Gait Detection in Parkinson''s (PD)","Digital health technologies (DHTs) have the potential to capture information about a person''s health, continuously in real time outside the clinic. This unlocks the ability to meet people where they are in their environment and at various stages along their health journey. When people are engaging with DHTs and the data are organized and analyzed to create new or better measures of health, there is opportunity for digitally-derived endpoints to extend clinical reach and facilitate early disease detection and prevention, or support disease treatment and management of care in the home.","","https://precision.fda.gov/challenges/33","completed","6","","2024-05-28","2024-08-02","\N","2024-05-21 22:03:46","2024-06-11 21:28:57"
 "521","placental-clock-dream-challenge","Placental Clock DREAM Challenge","Develop a new clock to achieve greater accuracy in predicting gestational age!","Since 2011, three generations of epigenetic clocks have been developed to estimate biological age[1-3]. The first-generation of models were generated by using DNA methylation data in various tissues to predict chronological age (outcome)[1, 4-6]. Second-generation models, such as PhenoAge and GrimAge used health outcomes, including all-cause mortality, for a more accurate determination of the latent biological age[7-9]. The latest, third-generation clocks like DunedinPoAm use longitudinal data to estimate the rate of aging[10]. This generation also includes universal clocks applicable to multiple species, such as the universal pan-mammalian epigenetic clock[11].  Biological age, as captured by these DNA methylation clocks, can be influenced by environmental factors, including smoking, obesity, sleep patterns, diet and exercise, stress, as well as diseases like cancer, diabetes, and Down syndrome[12-18]. The role of epigenetic programming in fetal development is crucial [19-21]. Th...","","http://synapse.org/placentalclock","completed","1","","2024-06-03","2024-08-27","\N","2024-06-03 16:59:54","2024-06-03 17:03:06"
-"522","detecting-active-tuberculosis-bacilli-2024","Detecting Active Tuberculosis Bacilli - 2024","","Tuberculosis is one of the leading infectious causes of death worldwide 1. Each year, millions of individuals contract and develop active TB without knowing 2. Case identification and treatment are the primary methods for controlling spread as there is no effective TB vaccine for adults. Unfortunately, delays in diagnosis are common, especially in resource-limited settings, and can worsen individual outcomes and perpetuate transmission of the disease 3,4. Without a timely diagnosis, patients needing treatment would head home from a clinic without knowing they were positive. If they miss their follow up, they would not learn about their diagnosis and nor would they start their treatment. Automated TB diagnosis could play a role reducing the loss of follow up and get patients to be treated more timely. Automated digital microscopy has been proposed as a cost-effective solution 6,7. An automated algorithm that could reliably detect mycobacterium on samples from patients with suspecte...","","https://app.nightingalescience.org/contests/m3rl61qq21wo","completed","15","","2024-03-01","2024-04-01","\N","2024-07-02 22:45:34","2024-07-02 22:47:34"
+"522","detecting-active-tuberculosis-bacilli-2024","Detecting Active Tuberculosis Bacilli - 2024","","Tuberculosis is one of the leading infectious causes of death worldwide 1. Each year, millions of individuals contract and develop active TB without knowing 2. Case identification and treatment are the primary methods for controlling spread as there is no effective TB vaccine for adults. Unfortunately, delays in diagnosis are common, especially in resource-limited settings, and can worsen individual outcomes and perpetuate transmission of the disease 3,4. Without a timely diagnosis, patients needing treatment would head home from a clinic without knowing they were positive. If they miss their follow up, they would not learn about their diagnosis and nor would they start their treatment. Automated TB diagnosis could play a role reducing the loss of follow up and get patients to be treated more timely. Automated digital microscopy has been proposed as a cost-effective solution 6,7. An automated algorithm that could reliably detect mycobacterium on samples from patients with suspecte...","","https://app.nightingalescience.org/contests/m3rl61qq21wo","completed","15","","2024-03-01","2024-04-01","\N","2024-07-02 22:45:34","2024-11-19 22:17:31"
+"523","isic-2024-challenge","ISIC 2024 - Skin Cancer Detection with 3D-TBP","Identify cancers among skin lesions cropped from 3D total body photographs","Skin cancer can be deadly if not caught early, but many populations lack specialized dermatologic care. Over the past several years, dermoscopy-based AI algorithms have been shown to benefit clinicians in diagnosing melanoma, basal cell, and squamous cell carcinoma. However, determining which individuals should see a clinician in the first place has great potential impact. Triaging applications have a significant potential to benefit underserved populations and improve early skin cancer detection, the key factor in long-term patient outcomes.  Dermatoscope images reveal morphologic features not visible to the naked eye, but these images are typically only captured in dermatology clinics. Algorithms that benefit people in primary care or non-clinical settings must be adept to evaluating lower quality images. This competition leverages 3D TBP to present a novel dataset of every single lesion from thousands of patients across three continents with images resembling cell phone photos....","","https://www.kaggle.com/competitions/isic-2024-challenge","completed","8","","2024-06-27","2024-09-06","2869","2024-11-19 22:20:29","2024-11-19 22:20:34"
+"524","czii-cryo-et-object-identification","CZII - CryoET Object Identification","Find small biological structures in large 3D volumes","Protein complexes (such as oxygen-carrying hemoglobin, or keratin in hair, and thousands of others) are essential for cell function, and understanding their interactions is essential for our health and finding new disease treatments. Cryo-electron tomography (cryoET) creates 3D images—called tomograms—at near-atomic detail, showing proteins in their very complex and crowded natural environment. Therefore, cryoET has immense potential to unlock the mysteries of the cell.  There is a wealth of cryoET tomograms that is yet to be fully mined. A large and growing portion of this published corpus exists in a standardized format in the cryoET data portal (cryoetdataportal.czscience.com). Mining this data requires automatic identification of each protein molecule within these images. This problem has not been solved even for proteins that are identifiable by the human eye. A generalizable solution will reveal the “dark matter” of the cell, and will enable thousands of discoveries contribu...","","https://www.kaggle.com/competitions/czii-cryo-et-object-identification","active","8","","2024-11-06","2025-02-05","2869","2024-11-19 22:20:36","2024-11-19 22:24:06"
+"525","1000-genomes-ancestry","1000 Genomes Ancestry","Predict the ancestry of individuals from the 1000 Genomes Project.","Welcome to the 1000 Genomes Project ancestry prediction competition.  Geneticists identified locations in the human genome that can assess an individual's ancestry, called ancestry-informative single nucleotide polymorphisms (AISNPs). These locations, denoted by rsid numbers, display ancestry-specific variation. For example, at location rs3737576, 32% of individuals from the 1000 Genomes Project who were assigned American ancestry have a C allele, while the study-wide frequency of the C allele was only 8%.  There are many locations like this across the human genome. AISNPs from two peer-reviewed publications are available in this competition. Kidd et al. identified 55 AISNPs, and Seldin et al. identified 128 AISNPs with varying degrees of discriminatory power. You can learn more about original ancestry classifications from the 1000 Genomes Project here and here.  The dataset in this competition contains alleles from the 1000 Genomes Project for 2,504 individuals across 183 AISNPs....","","https://www.kaggle.com/competitions/1000-genomes-ancestry","completed","8","","2024-04-18","2024-06-24","2416","2024-11-19 22:20:44","2024-11-19 22:27:53"
+"526","tcr-specificity-prediction-challenge","IMMREP23: TCR Specificity Prediction Challenge","Predictions on unpublished TCR-epitope binding to benchmark prediction methods.","IMMREP23, the second annual IMMREP benchmark on TCR-epitope specificity prediction will run from November 1, 2023 to December 11, 2023. Together with several experimental groups, we have compiled a data set of paired TCR data with annotated specificity to 21 pHLA (covering 6 distinct HLA molecules).  This challenge models TCR epitope recognition as a binary classification task. For a given test set of TCR-epitope pairs, the task of the model is to identify which pairs will bind and which will not bind.","","https://www.kaggle.com/competitions/tcr-specificity-prediction-challenge","completed","8","","2023-10-31","2023-12-11","2242","2024-11-20 16:02:13","2024-11-25 22:50:43"
+"527","ibiohash-2024-fgvc11","iBioHash 2024-FGVC11","A task of large-scale zero-shot fine-grained image hashing.","Fine-Grained Image Analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variations inherent to fine-grained image analysis make it a challenging problem.  Fine-grained image retrieval, as a crucial research area of FGIA, aims to retrieve images belonging to multiple subordinate categories of a super-category (aka a meta-category). Its key challenge therefore lies in understanding fine-grained visual differences that sufficiently distinguish objects that are highly similar in overall appearance, but differ in fine-grained features. Also, fine-grained retrieval still demands ranking all the instances so that images depicting the concept of interest are ranked highest based on the fine-grained details in the query.  In...","","https://www.kaggle.com/competitions/ibiohash-2024-fgvc11","completed","8","","2024-03-08","2024-05-17","2869","2024-11-25 22:36:58","2024-11-25 23:42:11"
+"528","mushroom-multiclass-classification","Mushroom multiclass classification","Create a model that classifies images of the most famous mushrooms from Estonia.","The primary motivation behind creating this competition was to address the challenge of mushroom identification, which poses a significant barrier to safe foraging and educational purposes. Misidentification of mushrooms can lead to health risks, including poisoning. By developing a reliable image classification model using this dataset, we aim to provide a tool that helps individuals accurately identify mushrooms, enhancing safety and promoting knowledge about local biodiversity.  Dataset The Estonian Mushroom Image Classification Dataset is a new collection of images representing the most popular mushroom species found in Estonia. It is structured into ten classes, each corresponding to a specific mushroom species, with annotations regarding their edibility status. The dataset consists of 300 images per class, totaling 3000 images.","","https://www.kaggle.com/competitions/mushroom-multiclass-classification","completed","8","","2024-04-14","2024-05-11","2869","2024-11-25 22:39:26","2024-11-25 22:51:37"
+"529","open-problems-single-cell-perturbations","Open Problems – Single-Cell Perturbations","Predict how small molecules change gene expression in different cell types","Human biology can be complex, in part due to the function and interplay of the body's approximately 37 trillion cells, which are organized into tissues, organs, and systems. However, recent advances in single-cell technologies have provided unparalleled insight into the function of cells and tissues at the level of DNA, RNA, and proteins. Yet leveraging single-cell methods to develop medicines requires mapping causal links between chemical perturbations and the downstream impact on cell state. These experiments are costly and labor intensive, and not all cells and tissues are amenable to high-throughput transcriptomic screening. If data science could help accurately predict chemical perturbations in new cell types, it could accelerate and expand the development of new medicines.  Several methods have been developed for drug perturbation prediction, most of which are variations on the autoencoder architecture (Dr.VAE, scGEN, and ChemCPA). However, these methods lack proper benchmar...","","https://www.kaggle.com/competitions/open-problems-single-cell-perturbations","completed","8","","2023-09-12","2023-11-30","\N","2024-11-25 22:45:56","2024-11-25 22:47:11"

apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv

@@ -646,3 +646,11 @@
 "645","publication","521","2024-06-03 16:59:54"
 "646","speaking_engagement","521","2024-06-03 16:59:54"
 "647","monetary","522","2024-07-02 22:45:34"
+"648","monetary","523","2024-11-19 22:20:29"
+"649","other","523","2024-11-19 22:20:29"
+"650","monetary","524","2024-11-19 22:20:36"
+"651","other","525","2024-11-19 22:20:44"
+"652","other","527","2024-11-25 22:36:58"
+"653","other","528","2024-11-25 22:39:26"
+"654","monetary","529","2024-11-25 22:45:56"
+"655","other","529","2024-11-25 22:45:56"

apps/openchallenges/challenge-service/src/main/resources/db/submission_types.csv

@@ -533,3 +533,13 @@
 "532","container_image","521","2024-06-03 16:59:54"
 "533","other","521","2024-06-03 16:59:54"
 "534","prediction_file","522","2024-07-02 22:45:34"
+"535","prediction_file","523","2024-11-19 22:20:29"
+"536","notebook","523","2024-11-19 22:20:29"
+"537","prediction_file","524","2024-11-19 22:20:36"
+"538","notebook","524","2024-11-19 22:20:36"
+"539","other","524","2024-11-19 22:20:36"
+"540","prediction_file","525","2024-11-19 22:20:44"
+"541","prediction_file","526","2024-11-20 16:02:13"
+"542","prediction_file","527","2024-11-25 22:36:58"
+"543","prediction_file","528","2024-11-25 22:39:26"
+"544","prediction_file","529","2024-11-25 22:45:56"