Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Investigating the Role of CNP and CNP aggregates in the Rheological Breakdown of Triglyceride Systems - Supporting Dataset

This dataset contains the files used to substantiate the outcomes of the publication "Investigating the Role of CNP and CNP Aggregates in the Rheological Breakdown of Triglyceride Systems"  The dataset includes: <ul> <li>X-ray scattering 2D profiles </li> <li>Thixotropy results</li> </ul> Relevant abbreviations:  <ul> <li>SSS - Tristearin</li> <li>PPP - Tripalmitin</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>PS - Palm Stearin</li> </ul>

opencc-by-sa-4.0Jun 2024View details →

MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)

Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a> The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84. File naming: <code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code> e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code> The date within the filename is year and month of aggregated timestamp. Pixel values: NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473 Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS 8.3.2 Format: GeoTIFF Original dataset license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-4.0Sep 2017View details →

CATCH-EyoU Work Package 2 Dataset 2.2a - Aggregation of Literature

The data is an aggregated dataset collected from all contributing partners, and includes a bibliography, concerning Europe, youth engagement and active citizenship across the disciplines represented in the Consortium (Political Science, Sociology, History, Media and Communications, Psychology and Education). The dataset contains reference to 770 individual texts. It is highly unlikely that a similar aggregated bibliography exists elsewhere. As this is new data, the reasons no suitable existing data are available is due to the breadth and scope of the aggregated dataset, which for the first time brings together literature from various disciplinary perspectives of young people and active citizenship across Europe, thus bridging a gap between previous studies that have mainly been limited to explorations of citizenship literatures within specific disciplines (and not across). The data sources are provided via csv document bibliography. The data set cleans and aggregates raw bibliographic data collected by consortium members during Months 1-3 of the research study. The aggregated literature dataset constitutes bibliographic data which can be reused by researchers who want to compare our data with similar data collected in different countries.

opencc-by-4.0May 2019View details →

Aggregated Virtual Patient Model Dataset

The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc. The dataset contains several medical features which are used by clinicians to assess the overall state of the older people. The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status. A list of the recorded clinical parameters and their description is shown below: - part_id: The user ID, which should be a 4-digit number - q_date: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z) - clinical_visit: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to - fried: Ordinal categorization of frailty level according to Fried operational definition of frailty - hospitalization_one_year: Number of nonscheduled hospitalizations in the last year - hospitalization_three_years: Number of nonscheduled hospitalizations in the last three years - ortho_hypotension: Presence of orthostatic hypotension - vision: Visual difficulty (qualitative ordinal evaluation) - audition: Hearing difficulty (qualitative ordinal evaluation) - weight_loss: Unintentional weight loss >4.5 kg in the past year (categorical answer) - exhaustion_score: Self-reported exhaustion (categorical answer) - raise_chair_time: Time in seconds to perform a lower limb strength clinical test - balance_single: Single foot station (Balance) (categorical answer) - gait_get_up: Time in seconds to perform the 3meters’ Timed Get Up And Go Test - gait_speed_4m: Speed for 4 meters’ straight walk - gait_optional_binary: Gait optional evaluation (qualitative evaluation by the investigator) - gait_speed_slower: Slowed walking speed (categorical answer) - grip_strength_abnormal: Grip strength outside the norms (categorical answer) - low_physical_activity: Low physical activity (categorical answer) - falls_one_year: Number of falls in the last year - fractures_three_years: Number of fractures during the last 3 years - fried_clinician: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire) - bmi_score: Body Mass Index (in Kg/m²) - bmi_body_fat: Body Fat (%) - waist: Waist circumference (in cm) - lean_body_mass: Lean Body Mass (%) - screening_score: Mini Nutritional Assessment (MNA) screening score - cognitive_total_score: Montreal Cognitive Assessment (MoCA) test score - memory_complain: Memory complain (categorical answer) - mmse_total_score: Folstein Mini-Mental State Exam score - sleep: Reported sleeping problems (qualitative ordinal evaluation) - depression_total_score: 15-item Geriatric Depression Scale (GDS-15) - anxiety_perception: Anxiety auto-evaluation (visual analogue scale 0-10) - living_alone: Living Conditions (categorical answer) - leisure_out: Leisure activities (number of leisure activities per week) - leisure_club: Membership of a club (categorical answer) - social_visits: Number of visits and social interactions per week - social_calls: Number of telephone calls exchanged per week - social_phone: Approximate time spent on phone per week - social_skype: Approximate time spent on videoconference per week - social_text: Number of written messages (SMS and emails) sent by the participant per week - house_suitable_participant: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer) - house_suitable_professional: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer) - stairs_number: Number of steps to access house (without possibility to use elevator) - life_quality: Quality of life self-rating (visual analogue scale 0-10) - health_rate: Self-rated health status (qualitative ordinal evaluation) - health_rate_comparison: Self-assessed change since last year (qualitative ordinal evaluation) - pain_perception: Self-rated pain (visual analogue scale 0-10) - activity_regular: Regular physical activity (ordinal answer) - smoking: Smoking (categorical answer) - alcohol_units: Alcohol Use (average alcohol units consumption per week) - katz_index: Katz Index of ADL score - iadl_grade: Instrumental Activities of Daily Living score - comorbidities_count: Number of comorbidities - comorbidities_significant_count: Number of comorbidities which affect significantly the person’s functional status - medication_count: Number of active substances taken on a regular basis

opencc-by-3.0Jul 2019View details →

Research data for: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials

Raw data for the publication: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials

opencc-by-2.0Oct 2019View details →

Wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer in tropical and arctic lattitudes

These data sets contain the retrieved wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer from two campaigns. The first campaign took place in the southern hemisphere at the Maïdo observatory on La Réunion Island (France), located in the Indian ocean at 21°S, 55°E. Data from April, May and June 2017 are included. For the second (and still ongoing) campaign, WIRA-C is located at the ALOMAR observatory on Andøya (Norway) at 69°N, 16°E. Data from September, October and November are included.

opencc-by-4.0Aug 2024View details →

ERA5-Land selected indicators daily aggregates for Africa, 1969

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1969. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Supplementary Files for "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals"

This repository hosts supplemental files for the Manuscript "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals" by Ruperti, et al., 2024. <ul> <li>Suppl_File_wreath_domain_model.pdb: AlphaFold3 model for the C. prolifera MAFp3 wreath domain (aa 33 - 317)</li> <li>Suppl_File_MAFAP1_Cterm_model.cif: AlphaFold3 model for the C. prolifera MAFAP1 C-terminal domain, region 1 and 2</li> <li>Suppl_File_AFInteracting_hmm.hmm: HMM sequence profile of AF-interacting region of C. prolifera proteins</li> <li>XXX_Foldseek.zip: Foldseek raw search results, separated by target databases (Swissprot, AFDB, CATH50)</li> </ul>

ERA5-Land selected indicators daily aggregates for Africa, 1964

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1964. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1965

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1965. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1966

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1966. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1968

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1968. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1967

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1967. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1959

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1959. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1962

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1962. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1952

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1952. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1960

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1960. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1963

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1963. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1951

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1951. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1956

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1956. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.