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gCube Catalogue (gCat) API is a library containing classes shared across gcat* components. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-api/releases/tag/v2.3.0

gCube Catalogue (gCat) Service allows any client to publish on the gCube Catalogue. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat/releases/tag/v2.4.1

gCube Catalogue (gCat) Client is a library designed to interact with REST API exposed by the gCat Service. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-client/releases/tag/v2.0.0

This release corresponds to the final version of the Mission Manager middleware component that has been developed for the AFarCloud European Research Project. It was tested and validated during the final demonstration in San Rossore in Pisa (Italy) held on the third week of November in 2021.

Radiosonde data from the RV Sonne cruise SO284: Tropical Atlantic Circulation and Climate: Mooring Rescue (June 27, 2021 - August 16, 2021) The dataset includes both raw data as well as post-processed products: Processing level Description Usage examples 0 mwx sounding files as delivered by Vaisalas sounding software Checking specific setup of sounding station, Archival of data 1 Level 0 data converted to netCDF4 Analysis of single soundings for the most accurate measurements possible 2 Level 1 data interpolated to a vertical grid Analysis of entire campaign or comparison with other observations or simulations A full description of the dataset and the processing scripts involved can be found in the following repository: https://github.com/observingClouds/soundings_circbrazil Soundings were conducted by H. Franke, I. Quaglia, K. Stolla and J. Windmiller. Technical support has generously been given on-board by R. Engelmann, J. Lehmke, T. Ruhtz and A. Skupin. Post-processing of the data has been done by H. Schulz. Planning of the cruise and the atmospheric measurement strategy are the work of J. Windmiller. Acknowledgment The crew of R/V Sonne greatly contributed to the success of the cruise. Financial support was provided by the German Science Foundation (DFG), by the EU H2020 under grant agreement 817578 TRIATLAS project and by the German Federal Ministry for Economic Affairs and Energy (BMWi) under grant no. 50EE1721C. We also acknowledge the public support of the conducted basic research through the Max Planck Society.

The set of Schemas defining the modeling of a novel Spatio-temporal-Semantic Data Model for Precision Agriculture IoT devices. Schemas for modeling observations captured by sensors or devices, collars fitted to the necks of livestock, and vehicle state-vectors are included.

This Release contains the final version of the DAM&DQ component of the Semantic Middleware for the AFarCloud smart agriculture platform. The data management is based on a novel Spatio-Temporal-Semantic data model proposal.

The Risk-behaviour map is a document intended to aid access to and transfer of key data between research groups in the European project Safeconsume. The map covers only steps from retail to consumption for the case studies in Safeconsume where the consumer can reduce risk for foodborne infection (CCHs, Critical Consumer Handling). The map contains information about observed/reported behaviours that can affect risk for foodborne infection divided by country, consumer group, pathogen, food and step in the journey from retail to consumption. Details about data collection is given in: Skuland, S.E., Borda, D., Didier, P., Dumitras¸cu, L., Ferreira, V., Foden, M., Langsrud, S., Maître, I., Martens, L., Møretrø, T., Nguyen-The, C., Nicolau, A. I., Nunes, C., Rosenberg, T. G., Teigen, H. M., Teixeira, P., Truninger, M., 2020. European Food Safety: Mapping Critical Food Practices and Cultural Differences in France, Norway, Portugal, Romania and the UK, in: Skuland, S.E. (Ed.). SIFO report, Oslo. ODA Open Digital Archive: European food safety: Mapping critical food practices and cultural differences in France, Norway, Portugal, Romania and the UK (oslomet.no) Questions about the RM-map can be raised to the SafeConsume project coordinator: Solveig.langsrud@nofima.no Variable list: Name Description CCH/Critical steps Identification of the step and flow diagram the entry belongs to: The step in the flow diagram where the consumer through actions or choices can significantly reduce risk of foodborne infection The CCHs/critical steps belong to one of the following processes: Poultry and vegetables (PVF), Eggs (EGG), Shellfish (SHE), Ready-to-Eat (RTE). Each step is accompanied by the principle of risk reducing effect: Food choice: Buy or eat food with lower risk (e.g avoid buying food if not stored properly in shop, buying pasteurised products, choosing to eat food before use-by-date). Applies to all pathogens. Inhibit growth: Storing ready-to-eat food at cool temperature and consume within expiration date or adding preservatives. Applies to Listeria and Salmonella Wash/Remove: Wash vegetables and fruit. Applies to all pathogens Kill/Heat: Heat treatment to kill pathogens, freezing (Campylobacter) Personal hygiene: Avoid cross-contamination through hand washing or not touching food. Not preparing food when sick Hygiene: Avoid cross-contamination through washing surfaces and using clean utensils Cause or sources Description of causes and sources for the hazard to occur (presence, survival, transfer or growth of pathogen). See Appendix 3 for details Consumer Id Unique identifier of consumer. Pathogen The pathogen(s) that are relevant for the specific CCH/critical step Expert opinion: Effect on pathogen Effect of behaviour on the hazard estimated by a team of microbiologists. Effect on pathogen The effect on pathogen is an estimate of the change in the level of viable pathogens as a direct or indirect consequence of the behaviour, action or process. Consumer group, education, income, rural/urban and country When applicable, demographic data associated with the entry. Classification Name Attributes Classification, llist of codes/units CCH/Critical step Predefined, multiple choices EGG 1 Food choice EGG 3.2 Hygiene EGG 4a Inhibit growth EGG 4b Inhibit growth EGG 5.1 Hygiene EGG 5.2 Personal hygiene EGG 6a Kill EGG 6b Kill EGG 6c Food choice EGG 6c Inhibit growth EGG 7.3 Inhibit growth EGG 8.3 Inhibit growth EGG 9.1 Inhibit growth EGG 11.3 Inhibit growth PVF 1.1 Food choice PVF 1.2 Food choice PVF 2.1 Inhibit growth PVF 3a Inhibit growth PVF 3a Kill PVF 3b Inhibit growth PVF 5.1 Personal hygiene PVF 5.2 Personal hygiene PVF 6.1 Kill PVF 7a Wash/Remove PVF 7b Personal hygiene PVF 7b Wash/Remove PVF 8b Hygiene PVF 8b Personal Hygiene PVF 9.1 Hygiene PVF 10.1 Hygiene PVF 11.1 Inhibit growth PVF 11.2 Inhibit growth RTE 1.1 Food choice RTE 3.1 Inhibit growth RTE 6.1 Inhibit growth RTE 4b Personal hygiene RTE 5.2 Hygiene RTE 5.2 Personal hygiene RTE 6.1 Inhibit growth RTE 7.1 Inhibit growth RTE 7.2 Personal hygiene SHE 1.1 Food choice SHE 7.1 Kill No risk Not designated to CCH Causes/sources Free text Consumer ID Free text Pathogen Predefined, multiple choice Salmonella: S. Enterica Campylobacter: C. jejuni Listeria: Listeria monocytogenes Norovirus Toxoplasma: Toxoplasma gondii Expert opinion: Effect on pathogen Predefined High reduction: This behaviour will have a high reduction on the level of pathogens on food/surfaces/hands Median reduction: This behaviour will reduce the level of pathogens on food/surfaces/hands No effect: This behaviour will most likely not have a significant effect on the level of viable pathogens (< 1 log10 reduction/increase or less than 10 cells/particles transfer) For food choice: Random choice is rated as no effect Median increase: This behaviour will lead to a higher number of pathogens High increase: This behaviour will significantly increase the number of pathogens on food/surfaces/hands Effect on pathogen comment Free text Consumer group Predefined Elderly; >70 years, men and women Pregnant women; immunocompromized Young family: Couples (married or cohabitant) where the women is pregnant or living with their own child(ren) (including stepchildren and adopted children) aged less than 12 months Young, single man: Men age 20-29, Living alone or with flatmates Other: Consumers not belonging to the defined groups Educational level Predefined Basic Secondary Tertiary Not given Living area Predefined Rural Urban Income Predefined Low Median High Country Predefined Portugal, France, Romania, UK, Norway, Hungary, More details about the dataset can be achieved from the authors

RINEX3 (https://files.igs.org/pub/data/format/rinex305.pdf) Hatanaka-compressed (http://sopac.ucsd.edu/hatanaka.shtml) GNSS (Global Navigation Satellite System) data of single-frequency observations obtained by the Ublox Neo M8T receiver (Evaluation Toolkit) and standard Ublox patch antenna. The data is downsampled to 15-second observations spanning from DOY (day of year) 95 in 2017 to DOY 83 in 2020. The unit was placed on the rooftop of the observatory of the NMi building in Delft (approx. coordinate 51.986166, 4.387678). The receiver was connected to a constant power supply. The antenna was placed on the flat rooftop (no significant obstruction in the sky) with a 10cm circular glound plane underneath.

This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir. The supp_CODES.zip contains all details and codes to use COSI-CORR (http://www.tectonics.caltech.edu/slip_history/spot_coseis/) to process a large batch of satellite images. The images can be downloaded directly via https://earthexplorer.usgs.gov/ or https://scihub.copernicus.eu. Please read the Methods and Data section of the associated manuscript for details. The Matrix_velocities.zip contains, for each of the 48 investigated glaciers, the DEM, X, Y (NANNI_2022_supp_glacier_centreline_DEM_XY_1px_30m_1.txt) as well as a matrix of n*m with m the distance along flow and n the number of time step over which the velocity is calculated (NANNI_2022_supp_glacier_centreline_vel_matrix_1px_30m_1.txt), ans the associated figure that show the multi year velocity changes together with the one year average and the along centreline profiles. An example is shown in the two figures for glacier 48 in the main repository. The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures. The NANNI_2022_supp_pickedpoints_migration_AUTUMN/SPRING contains the automatically picked points for the onset of the acceleration in Spring and Autmun for each glacier. The headers contains the information, and the files contains is shown in the associated figure. the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m The position of each 48 glacier is shown in the associated figure. You can also find the processed velocity fields (velocity magnitude) under the different path an row: p151r33.zip and p152r33.zip for Landsat8, T42SYJ.zip and T43SBD.zip for Sentinel 2. In these folder you will a find a .tif file names similar to: Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif The name of the files gives information about the time span used (16days), the path and raw (p152r33), the data of the slave in DOY from 2013 (159) and of the master (175). The Statistics.zip file contains for each path and row the associated DEM, glacier mask (RGI), median magnitude (ABS), median NS displacement (NS), median EW displacemnt (EW), with the associated median absolute deviation (MAD). The files containing 'bflt' corresponds to the values computed before the filtering procedure, and the one without, after the filtering procedure. The .tif files are not georeferenced, but are all projected on the same grid with a 30m square pixel size on a UTM 33 42N projection. Please contact me for any question.