The GenoMEL data commons supports the management, analysis and sharing of next generation sequencing data for the GenoMEL research community and aims to accelerate opportunities for discovery of susceptibility genes for melanoma. The data commons supports cross-project analyses by harmonizing data from different projects through the development of a data dictionary and utilization of common workflows, providing an API for data queries, and providing a cloud-based analysis workspace with rich tools and resources.

At its core, TDC collects ML tasks and associated datasets across therapeutic modalities and stages of discovery. These tasks and datasets have the following properties: Instrumenting disease treatment from bench to bedside with AI/ML: TDC covers a variety of learning tasks going from wet-lab target identification to biomedical product manufacturing. Building off the latest biotechnological platforms: TDC is regularly updated with novel datasets and tasks, such as antibody therapeutics and gene editing. Providing AI/ML-ready datasets: TDC datasets provide rich information on biomedical entities. This information is carefully curated, processed, and readily available in TDC.

This parent dataset (collection of datasets) describes the general organization of data in the datasets for each growing season (year) when alfalfa (Medicago sativa L.) was grown as a reference evapotranspiration (ETr) crop at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Alfalfa was grown on two large, precision weighing lysimeters, calibrated to NIST standards (Howell et al., 1995). Each lysimeter was in the center of a 4.44 ha square field on which alfalfa was also grown (Evett et al., 2000). The two fields were contiguous and arranged with one (labeled northeast, NE) directly north of the other (labeled southeast, SE). See the resource "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. Alfalfa was planted in Autumn 1995 and grown for hay in 1996, 1997, 1998, and 1999. The resource "Agronomic Calendar for the Bushland, Texas Alfalfa Datasets", gives a calendar listing by date the agronomic practices applied, severe weather, and activities (e.g. planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest) in and on lysimeters that could influence crop growth, water use, and lysimeter data. These include fertilizer and pesticide applications. There is one calendar, from before planting in autumn 1995 to after final harvest in 1999, for the NE and SE lysimeters and fields. There were 4 harvests each year except 1998 when 5 harvests were taken. Irrigation was by linear move sprinkler system equipped with pressure regulated low pressure sprays (mid-elevation spray application, MESA). Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings via field-calibrated (Evett and Steiner, 1995) neutron probe from 0.10- to 2.4-m depth in the field. Lysimeters and fields were planted to the same plant density, row spacing, tillage depth (by hand on the lysimeters and by machine in the fields), and fertilizer and pesticide applications. Weighing lysimeters measured relative soil water storage to 0.05 mm accuracy at 5-min intervals, and the 5-min change in soil water storage was used along with precipitation, dew and frost accumulation, and irrigation amounts to calculate crop evapotranspiration (ET), reported at 15-min intervals. Each lysimeter was instrumented to sense wind speed, air temperature and humidity, radiant energy (incoming and reflected, typically both shortwave and longwave), surface temperature, soil heat flux, and soil temperature, all at 15-min intervals. Instruments used changed from season to season, thus subsidiary datasets and data dictionaries for each season are required. The Bushland weighing lysimeter research program is described by Evett et al. (2016), and lysimeter design is described by Marek et al. (1988). Important conventions concerning the data-time correspondence, sign conventions, and terminology specific to the USDA ARS, Bushland, TX, field operations are given in the resource "Conventions for Bushland, TX, Weighing Lysimeter Datasets". There are 5 datasets in this collection. Common symbols and abbreviations used are defined in the resource "Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets". Datasets consist of Excel (xlsx) files. Each xlsx file contains an Introductory tab that explains the other tabs, lists the authors, describes conventions and symbols used, and lists instruments used. The remaining tabs in a file consist of dictionary and data tabs. The 5 datasets are:

Growth and Yield Data for the Bushland, Texas Alfalfa Datasets Weighing Lysimeter Data for The Bushland, Texas Alfalfa Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Alfalfa Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas

See README for descriptions of each dataset. The soil is a Pullman series fine, mixed, superactive, thermic Torrertic Paleustoll. Soil properties are given in the resource titled "Soil Properties for the Bushland, TX, Weighing Lysimeter Datasets". Land slope in the lysimeter fields is

This list was generated 4/9/21. For an up-to-date representation of the schema query the Biomedical Data Commons graph or browser or check the github repository. (XLSX)

The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

Social cooperation often requires collectively beneficial but individually costly restraint to maintain a public good, or it needs costly generosity to create one. Status quo effects predict that maintaining a public good is easier than providing a new one. Here, we show experimentally and with simulations that even under identical incentives, low levels of cooperation (the 'tragedy of the commons') are systematically more likely in maintenance than provision. Across three series of experiments, we find that strong and weak positive reciprocity, known to be fundamental tendencies underpinning human cooperation, are substantially diminished under maintenance compared with provision. As we show in a fourth experiment, the opposite holds for negative reciprocity ('punishment'). Our findings suggest that incentives to avoid the 'tragedy of the commons' need to contend with dilemma-specific reciprocity.

This dataset contains the ground truth data used to evaluate the musical pitch, tempo and key estimation algorithms developed during the AudioCommons H2020 EU project and which are part of the Audio Commons Audio Extractor tool. It also includes ground truth information for the single-eventness audio descriptor also developed for the same tool. This ground truth data has been used to generate the following documents: Deliverable D4.4: Evaluation report on the first prototype tool for the automatic semantic description of music samples Deliverable D4.10: Evaluation report on the second prototype tool for the automatic semantic description of music samples Deliverable D4.12: Release of tool for the automatic semantic description of music samples All these documents are available in the materials section of the AudioCommons website. All ground truth data in this repository is provided in the form of CSV files. Each CSV file corresponds to one of the individual datasets used in one or more evaluation tasks of the aforementioned deliverables. This repository does not include the audio files of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed. Structure of the CSV files All CSV files in this repository (with the sole exception of SINGLE EVENT - Ground Truth.csv) feature the following 5 columns: Audio reference: reference to the corresponding audio file. This will either be a string withe the filename, or the Freesound ID (for one dataset based on Freesound content). See below for details about how to obtain those files. Audio reference type: will be one of Filename or Freesound ID, and specifies how the previous column should be interpreted. Key annotation: tonality information as a string with the form "RootNote minor/major". Audio files with no ground truth annotation for tonality are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Tempo annotation: tempo information as an integer representing beats per minute. Audio files with no ground truth annotation for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Note that integer values are used here because we only have tempo annotations for music loops which typically only feature integer tempo values. Pitch annotation: pitch information as an integer representing the MIDI note number corresponding to annotated pitch's frequency. Audio files with no ground truth pitch for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. The remaining CSV file, SINGLE EVENT - Ground Truth.csv, has only the following 2 columns: Freesound ID: sound ID used in Freesound to identify the audio clip. Single Event: boolean indicating whether the corresponding sound is considered to be a single event or not. Single event annotations were collected by the authors of the deliverables as described in deliverable D4.10. How to get the audio data In this section we provide some notes about how to obtain the audio files corresponding to the ground truth annotations provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these ground truth annotations. Apple Loops (APPL): This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here. Carlos Vaquero Instruments Dataset (CVAQ): This dataset includes single instrument recordings carried out by Carlos Vaquero as part of this master thesis. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs Freesound Loops 4k (FSL4): This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are provided here. Giant Steps Key Dataset (GSKY): This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations available here. Good-sounds Dataset (GSND): This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are available here. University of IOWA Musical Instrument Samples (IOWA): This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by visiting this website. Mixcraft Loops (MIXL): This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to thes...

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According to 35 percent of Chief Information Security Officers (CISO) from worldwide organizations, an employee or a so-called compromised insider that might inadvertently expose their credentials, giving cybercriminals access to sensitive data, was the most common cause of a data breach. A further 33 percent thought a malicious insider, who would intentionally steal the information would most likely cause a data breach in their organization in the next 12 months.

BackgroundBreast cancer (BC) remains a significant health burden globally, with high incidence and mortality rates, particularly in Nigeria. Chemotherapy, a common treatment modality for BC, often leads to various physical and psychological side effects, impacting patients’ quality of life. Despite the growing use of mobile health (mHealth) interventions to provide psychoeducational support, there is a paucity of evidence regarding their feasibility and acceptability among Nigerian women with BC.ObjectiveTo develop and investigate the feasibility and acceptability of a mHealth psychoeducational intervention (mPEI) named the ChEmo Nurse Breast cancer Application (CENBA) programme.MethodsA multi-centre, assessor-blinded, parallel-group pilot randomised controlled trial (RCT) was conducted at Lagos State University Teaching Hospital (LASUTH) and Lagos University Teaching Hospital (LUTH). Thirty women newly diagnosed with BC and undergoing chemotherapy were randomly assigned to an intervention or a control group. The intervention group received the CENBA programme, which included BC education, coping skills training, a discussion forum, and nurse-led consultations, delivered via a mobile application and phone calls over six weeks. The control group received standard care. Feasibility was assessed through consent, attrition, and completion rates, while acceptability was explored via qualitative interviews.ResultsThe completion rate was 93.3%. Qualitative data indicated that participants found the intervention beneficial, particularly appreciating the educational content and the emotional support provided through the discussion forum and nurse consultations.ConclusionThe CENBA programme was perceived as a feasible and acceptable mHealth intervention for providing psychoeducational support to Nigerian women with BC undergoing chemotherapy. These findings suggest that the CENBA programme could be a valuable tool in addressing the psychoeducational needs of this population, warranting further investigation in a full-scale RCT.Trial registrationThis manuscript reports a feasibility study preceding the full trial, which was registered with the United States Clinical Trials registry (number NCT05489354).