In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry). The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented. The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation. Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app’s source code, it offers a platform to integrate new features.

This dataset was intially aimed for publication on GBIF (see details below), but we have now restricted it to a 'metadata' entry, and the corresponding ecosystem dataset is published on Zenodo: https://doi.org/10.5281/zenodo.7812549. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are:

1. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies.

2. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences.

3. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach).

Although GBIF is currently not able to cater appropriately for ecosystem data and is designed in a species-centric view, GBIF is the largest repository of biodiversity data in the world and therefore it is relevant to at least explore the possibility of addressing that gap. In addition, as we will show here, we suggest that only a few additions and adjustments to the current GBIF structure would be required to integrate the treatment of ecosystem data in a standardized way, following the "ecosystemology" approach (ecosystem taxonomy) proposed by Senterre et al. 2021b (http://dx.doi.org/10.1016/j.ecocom.2021.100945).

In the ‘sampling method’ section of these metadata, we present in detail the suggested needs for adjustments and additions in the GBIF structure, and we explain our short term strategy to publish an existing ecosystemology dataset using the current GBIF structure, by squeezing information within available and suitable fields of GBIF (mostly free text fields that are related to the ecosystem or habitat). Several fields are thus stored within a GBIF field by using the pipe separator (|).

We then developed a series of R scripts that take the ecosystem data squeezed into the GBIF fields and that restore the tables needed to do an ecosystem taxonomy treatment (by splitting columns at the pipe separators). Finally, we compile ecosystem checklists, taxonomies and occurrence data into an R shiny application. In addition, we integrate the use of Google Earth Engine (EE) and we develop the method to integrate these with the GBIF dataset toward the production of complete distribution maps and their use in Red Listing of Ecosystems (RLE).

The R scripts developed are available here: https://github.com/bsenterre/ecosystemology

The corresponding shiny app is available here: https://shiny.bio.gov.sc/bioeco/ (earlier version : https://bsenterre.shinyapps.io/ecosystemology/)

The EU Horizon 2020 project DiverIMPACTS aims to promote the realisation of the full potential of crop diversification through rotation, multicropping and intercropping by demonstrating technical, economic and environmental benefits for famers, along the value chain and for society at large, and by providing innovations that can remove existing barriers and lock-ins of practical diffusion.

DiverIMPACTS does so by combining findings from several participatory case studies with a set of field experiments across Europe, and translating these into strategies, recommendations and fit-for-purpose tools developed with and for farmers, advisors and other actors along the value chain.

To first gain a good overview of the current situation, i.e. the existing success stories and challenges of crop diversification in Europe, Work Package 1 (WP 1) identified and analysed factors of success and failure associated with a variety of crop diversification experiences (CDEs) outside those already represented in the consortium (see Deliverable 1.1). WP 1 thus makes sure that the rich experience with crop diversification initiatives across Europe (e.g. from other Horizon 2020 projects) is taken into account for developing strategies, recommendations and tools.

Deliverable 1.1 provided i) a list of key drivers (ex ante occurrence of market opportunities, environmental constraints, availability of enabling advisory services, land and workforce availability etc.) to be further considered in WP3, and WP5; and ii) a comprehensive and exhaustive description of the links between key factors and CDE types. This analysis is the basis for consolidating or updating the tentative typology of crop diversification situations used for setting up DiverIMPACTS (case studies), and was used for selecting experiences for more detailed investigations in T1.2. It also complements the identification and characterisation of lock-ins and barriers to crop diversification, and serves their overcoming. During the process of collecting, cleaning and analysing the survey data, a Database of European diversification experiences was created.

All together 128 valid responses from 15 European countries – mainly from the project countries Belgium, France, Germany, Hungary, Italy, the Netherlands, Poland, Romania, Sweden, Switzerland, and UK, but also from Denmark, Finland, Luxemburg and Spain were received in T1.1, and were included in the database.

The database is stored in original and back-up form in a tabular ='.csv'= format that can be opened in Excel on the Sharepoint system of the project and now on Zenodo, under restricted WP1 area. A further ='.csv'= file was created to store the metadata of the database. This file helps to have a better overview of the questions and sub-questions that were asked in the survey and the type of answer that could be provided to each of them (e.g. factor, Yes-No selection or character).

Using the meta data and the database, a selection of personal data fields has been made (e.g. email addresses and names of people) that cannot be published with open access, and needs special attention and data handling. These variables were removed from the original database, and a public version of the database was created that can be shared with third parties. Links to the data files will be shared here after.

Developing a Shiny(c) application in R was chosen as a solution to visualize the public data, and make it possible for Partners and all interested parties to interactively view the survey results. The Shiny application is shared as an R-package and are freely accessible on the internet. The users have the possibility to download application and public data in order to visualize them on their own computer. A remote solution, facilitating the consultation of the data, will be installed in CRA-W, where the open data analyses module will be hosted. A short user guide and tutorial is part of this deliverable for helping interested parties to use the Shiny interface.

The chosen approach, linking R scripts, R packages and data files, will be useful in the future in order to continiously complete the data base and to update the application (new graphs, new functions regarding the demand of the main users). The release of the application will be shared using modern technologies of information and communication : project website, newsletter, blogs, twitter and other social networks.

The main deliverable (D1.2) which is public, is available here : 10.5281/zenodo.3966852