dataesr/scientific-paragraphs-categorization huggingface dataset:
Best models for the CF ET Using our methodology, we could exhibit a median absolute error of 0. 62 log for the prediction of the CF ET on the whole dataset using the best models. If we looked closer on Table  S3 , we could see that the median of our estimations is below 0. 6 log except for cluster 1 (above 1 log). Then we calibrated the best models on the whole dataset of each cluster: a cluster-then-predict approach using SVM for clusters 1 and 4 and using random forest for clusters 2 and 3. To compare the different models in each cluster and give an idea of what were the important molecular descriptors we provided the five most important molecular descriptors for each cluster in the Table  S4. We could see in this table that the important molecular descriptors strongly differ from one cluster to another.
Then the models were used to predict the missing CF ET of the common compounds between USEtox® and TyPol databases. These values were by consequence new estimations of the CF ET for compounds on which we had no information. The prediction intervals were relatively small: less than 0. 5 log 10 in a log scale which highlighted the robustness of the estimation. They are given in Table  S5. No NA value was present in cluster 1 with no prediction for this cluster. For cluster 2 gathering molecules with intermediate molecular mass, 9 CF ET values were predicted for various kinds of compounds. One value concerns the antibiotic sulfamethazine and its value is quite near to the one of sulfamethoxazole and sulfadiazine of the same sulphonamide antibiotic family constituted of the sulphonamide group (-S(=O) 2 -NR 2 R 3 ).
Best models for the CF HT The best models had a global median of 0. 75 log for the prediction of the CF HT. We could see on the results gathered on Table  S6  that the best performances are for cluster 1 and that they are comparable for the other clusters. Then, the global SVM model was calibrated and computed on the whole dataset. It was used to predict the compound of clusters 2, 3, 4, and 5. Let us recall that there was a single molecule in cluster 5 and, as it has a NA value for its CF HT , the best global model (SVM) was used. For cluster 1, a cluster-then-RF model was computed. The most important descriptors of these two models are gathered in the Table  S7  and, as for CF ET , were strongly different between the different best models. Then, this model was used to predict the CF HT value for the 102 common compounds without a CF HT value.
g. random forest) more interpretable  (Bénard et al. , 2021). The difficult interpretability of the machine learning models used in this study can thus be viewed as a limitation. On another side, even if we already had an acceptable number of compounds in our training datasets, the model accuracies would benefit of the inclusion of new compounds. These compounds could be carefully chosen to improve the models where there is a clear need (i. e. where the performances of the models are not good enough), for example in the cluster 1 for CF ET or in the cluster 4 for CF HT. One of the interests of USEtox® and its three-step structure (fate -exposure -effect) is that it can be adapted to some specific contexts (a more accurate and spatialized fate model, a different exposure. ) while keeping the steps that are not modified.
Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them. FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research, is the subject of the paper.
Supporting the call for the need for a "one-stop-shop and register-onceuse-many approach", we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS.
, 2022) ). Here, FAIR principles become a promising asset. The FAIR principles were originally developed as guidelines or recommendations for the effective and efficient management of research data and stewardship as part of a new open science policy framework, with a "specific emphasis on enhancing the ability of machines to automatically find and use the data" in data repositories  (Wilkinson et al. , 2016). Since then, FAIR principles have become central element in the debate and implementation of open science policies, and they are increasingly being applied to other "digital objects"  (Wittenburg, 2019)  of different levels such as institutional repositories and large infrastructures such as European Open Science Cloud (EOSC) and the German NFDI (Nationale Forschungsdatenin-frastruktur -the mandated organisation for Germany in the EOSC), but also to metadata, identifiers, catalogs, software, or scientific practice.
As outlined in the UNESCO Recommendation on Open Science, FAIRness has become an essential feature of what has been called "open science culture" over the past couple of years. Promoting a culture of open science requires, for example, the development of rewards and incentives that "give value to all relevant research activities and scientific outputs including high-quality FAIR data and metadata" (UNESCO, 2021). At the moment, however, EOSC have begun their own research to define and introduce guidelines to application of FAIR principles to digital objects not necessarily limited to the research domain, thereby expanding the scope to the entire digital environment and all data and databased objects (also referred to as "digital objects"), including research objects such as scientific articles and software, available on the Internet, while the main focus remains on the research domain.
In this study, however, we refer to the research domain and FAIRness of research information systems (RIS). Research information management systems or Current Research Information Systems (hereafter CRIS) are seen as "core elements of the technological solution since they provide rich additional metadata on datasets and put the datasets and their metadata into their proper context, and so significantly enhance the FAIRness of datasets"  (Terheggen and Simons, 2016). In other words, CRIS are not just data repository, but a key component of the research infrastructure landscape, which is directly targeted and involved by the operational application and promotion of the FAIR principles. This involvement can be described at three levels constituting a set of propositions of our study on which we will elaborate within this paper: basis of scientific knowledge processes, but is also related to other data, research data management (RDM) is becoming more and more important to enable and ensure comprehensive access to data stocks so that research results can not only be verified and interpreted, but it can also be understood how these results were obtained and how they can be made usable and actionable.
, 2015). For instance, the OECD has published guidelines on access to research data for public funding  (OECD, 2007) , and the Alliance of German Science Organizations has adopted principles for handling research data  (Society, 2010). The EU funding program Horizon 2020 has introduced FAIR data management  (Commission, 2020a)  guidelines / recommendations, and the new Horizon Europe program confirms that research data management cannot be "opt-out" and that projects generating research data must manage their data responsibly and in line with FAIR principles  (Commission, 2020c). One of the main goals of open science is to increase the transparency and accessibility of scientific results  (Paic, 2021). The FAIR data principles set the standard for the sustainable use of research data, insofar metadata are crucial for the later interpretability, interoperability and traceability of data, including quality assurance and control measures.
The ever-increasing relevance of data for research requires new and efficient strategies for processing and handling research information and research data. For this purpose, the National Research Data Infrastructure (NFDI) is currently building a digital knowledge repository for the development, networking and use of research data in Germany  (Wachtler et al. , 2021). This includes, for example, making data col-lection or RDM transparent and understandable. This is important given that in universities and research institutions it is often unclear what methods are used to collect the data or how their trustworthiness is ensured  (Guba and Lincoln, 1989) ,  (Azeroual and Schöpfel, 2021). In this sense, the context of the data is of interest, so information about the collection strategy, as well as their structure, is essential.
, 2019). With the growing volume of data and the emerging cross-disciplinary challenges, the need for a consistent, interoperable framework for documenting data that can reliably find, filter and compare research information of different origins is becoming increasingly evident. Largely supported by Artificial Intelligence (AI), Big Data analytics and Machine Learning (ML) methods require data to be not only accessible to humans, but also be machine-readable and understandable  (Nguyen et al. , 2018). This requires a selection of standardized, long-life readable data formats, as well as clear coding of the metadata descriptions, standardizing the two attribute fields and their permissible content (attribute values). The four FAIR data principles, introduced by RDM, specify goals, but do not provide solutions for this non-trivial task.
The FAIR data principles describe an ecosystem of data, metadata, scientific software, but also workflows, metrics, and the need for continued funding of infrastructures that are consistent with the implementation of the FAIR principles (e. g. repositories)  (Arefolov et al. , 2021). The unique identification of data (items), people, institutions, projects, as well as policies, guidelines, standards and data repositories are indispensable tools in such a system. The question implies from the above -whether and how can CRIS contribute to promotion of FAIRness?.
Moreover, the results obtained using different tools tend to differ significantly with a very vague understanding on what should be done to improve the result if another tool assessed the level of FAIRness as sufficient. All in all, such "ad-hoc" tools may be more or less useful for the follow-up of local FAIR programs or for training FAIR principles, thereby developing FAIR literacy. However, they create new information silos and in most cases are not linked to professional assessment systems such as CRIS. Open data practices and the FAIRness of research data have become essential characteristics of research performance. The European Commission's Open Science Monitor includes national level information on open research data  (Commission, 2020b). "Using the FAIR data principles" is one of the evaluation criteria proposed in OS-CAM, the Open Science Career Assessment Matrix  (O'Carroll et al.
, 2017) , (European  Commission and Innovation, 2021). The CRIS can and even should collect, aggregate and integrate structured and carefully curated information on research data and its FAIRness to support the monitoring and assessment of this element. For the purpose of this study, we selected euro-CRIS repository -as a major source used by euro-CRIS community, which participants represent practitioners dealing with the actual national, regional and international CRIS -as a source for conducting a extracting the most relevant studies with an explicit elaboration of FAIR principles and their implementation within CRIS. All in all, after a systematic literature review, three studies were found to be relevant to illustrate this approach. In other words, all relevant studies available in euroCRIS repository were selected and studied.
CONTRIBUTING TO THE FAIRNESS OF RESEARCH INFRASTRUCTURES The current body of knowledge, i. e. scientific literature on the topic, suggests that CRIS can contribute to improving the FAIRness of other research infrastructures, such as data repositories or publication platforms. One of the main reasons for this potential is the central position of CRIS in the research information ecosystem  (Donohue et al. , 2018). In short, CRIS obtains data from a wide range of external and internal sources such as scientometric databases, library catalogs, human resources, finance, project management etc. , and provides the data or information in standardized formats to other infrastructures and research information tools. This potential is not a general and common characteristic of all types of CRIS -it rather depends on the degree of standardization of the CRIS data model and the CRIS format.
IMPROVING THE FAIRNESS OF CRIS In other words, while FAIR is typically discussed at three levels -(1) digital object, which refers to dataset, videos, journals, books etc. , (2) metadata about this object on elementary level, including title, creator, identifier, etc. , and (3) metadata records with the reference to the body of metadata element on the object in a specific database  (Engelman et al. , 2019) , we suggest refer not only to data and information, but also to the upper level of the data or information management systems, i. e. CRIS. Thus, our third proposition is that CRIS itself can be improved by following and / or applying FAIR principles on it. In other words, CRIS is not only suitable to improve the FAIRness of research data management, but the FAIR principles are also beneficial for the further development of sustainable and FAIR CRIS.
DISCUSSION AND CONCLUSIONS This paper focuses on FAIR data principles and their potential and real contribution to the quality and the reusability of research information with the further possibility of creating knowledge from it and an efficient knowledge management, which is only possible if the list of the discussed prerequisites is met. It is crucial that the research information is available in such a way that it can be found, accessed, linked and reused as easily as possible (for authorized users) thereby being as FAIR as possible. Essentially, it is a quality requirement for data management as a prerequisite for being able to use the data, which are often obtained with great effort, which quality is not preserved when the data are stored and further processed thereby losing their value.
Research information is not just research data, and research information management systems such as CRIS are not just repositories for research data. They are much more complex, alive, dynamic, interactive and multi-stakeholder objects. However, in the realworld they are not directly subject to the FAIR research data management guiding principles. But as described above, CRIS are part of the research infrastructure ecosystem and are linked to data repositories, where the idea of CRIS partly overlaps with the main goal of FAIR principles, where the original scope of CRIS is more limited. At the same time, the scope of the FAIR principles has been extended recently to include a broader variety of "digital objects", infrastructures and content. For both reasons, CRIS can (and already does) improve the FAIRness of research infrastructures and data through the evaluation (monitoring) and standardization of data and metadata.
In this paper we have raised a discussion on this topic showing that the improvement of FAIRness is a dual or bidirectional process, where CRIS promotes and contributes to the FAIRness of data and infrastructures, and FAIR principles push for further improvement in the underlying CRIS data model and format, positively affecting the sustainability of these systems and underlying artifacts. CRIS are beneficial for FAIR, and FAIR is beneficial for CRIS. Nevertheless, as pointed out by  (Tatum and Brown, 2018) , the impact of CRIS on FAIRness is mainly focused on the (1) findability ("F" in FAIR) through the use of persistent identifiers and (2) interoperability ("I" in FAIR) through standard metadata, while the impact on the other two principles, namely accessibil-ity and reusability ("A" and "R" in FAIR) seems to be more indirect, related to and conditioned by metadata on licensing and access.
, 2021). For further research, more case studies are needed to explore the potential of research information management to monitor FAIR projects and infrastructures at the local, regional, national and international levels. More empirical evidence needs to be presented on the real and specific impact of CRIS on the development of FAIR data repositories and other research infrastructures, with a particular focus on standardization. Finally, further development of CRIS data models and formats should focus on FAIR principles, especially findability and interoperability, in an explicit way. At the same time, the ethical and legal aspects of accessibility of CRIS data require further investigation to get a full picture of what it really means to apply the FAIR principles to research information management.
Cosmological information from weak lensing surveys is maximised by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present , a new method for marginalising over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parameterized models. In the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalising over discrete realisations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency.
Current galaxy surveys rely on a combination of spectroscopic and photometric redshifts, plus clustering patterns, to train, calibrate, and validate different methods. These methods can be broadly classified into three types, based on the information and ancillary data used to estimate redshift. (i) Template fitting methods (see Section 3. 1 of  Schmidt et al. 2020 , for a review) which rely on finding the best-fit template redshift from a library of spectral energy distributions (SED) characterising a range of galaxy types. (ii) Machine learning based techniques (see Section 3. 2 of  Schmidt et al. 2020 , for a review) which map the colour space into redshifts. While the range of approaches used is fairly wide, the general idea consists of using a training set of secure redshifts obtained using either spectroscopy or large sets of narrow-band filter photometric observations to train the algorithm.
Alternatively, we may explicitly parameterise the () as a set of histogram bin heights (  ) which give the counts of sources within a small redshift interval and try to infer these quantities from the data. This approach creates principled models of the joint probability distribution function for all of these bin heights given the photometric data available on the observed galaxies. This is most readily done as a Bayesian Hierarchical Model and has been recently advocated in  Leistedt et al. (2016) ,  Sánchez & Bernstein (2019)  and  Rau et al. (2020). Outputs from this procedure are samples from the joint posterior for all of the histogram bin heights which together make up the full shape of the (). Each sample consists of a possible realisation of what the full () could look like, discretised as (  ).
(2021) ;  Secco, Samuroff et al. (2021). We only consider cosmic shear in our data vector, which reduces the dimensionality of the space of parameters to be sampled in the MC inference and enhances the effect of redshift systematics in the source sample. Nevertheless, this method can be applied when using cosmic shear in a full 3x2pt analysis including galaxy clustering and galaxy-galaxy lensing and can also be used to marginalise over systematic uncertainties of the lens in addition to the source samples described here.
SOMPZ redshift distributions The simulated photometry catalogues from B constitute the primary dataset to construct the fiducial () for our tests, using the SOMPZ method (fully described in  Myles, Alarcon et al. 2020 ). This method makes use of three sets of observations: the full DES-Y3 wide field sample, the DES-Y3 Deep Fields  (Hartley, Choi et al. 2020 ) sample, and compilation of spectroscopic redshift surveys. Galaxies from the wide sample are grouped into phenotypes using the Self-Organised Maps (SOM) method of dimensional reduction (see e. g. Myles, Alarcon et al. 2020; Wright et al. 2020; Masters et al. 2015). The Balrog machinery (which injects synthetic sources into DES data and recovers their properties, see  Everett et al. 2020 ) is then used to quantify the probability of a given Deep Fields galaxy appearing to have a given phenotype when observed in the wide field.
APPLICATION TO DES YEAR 3 Based on the above tests, we derive an appropriate configuration for using on DES-Y3 (or similar) data: •  ranking • Three parameter dimensions • Ranking according to tomographic bins 1, 2 and 4 • At least 10 3 () samples available to We then run a full shear-only cosmology chain on the B simulation of the DES-Y3 data set, with model parameterisations and priors as discussed in the main cosmology papers  (Amon et al. 2021; Secco, Samuroff et al. 2021) , and with 1000 realisations of possible () generated using the full procedure of  Myles, Alarcon et al. (2020). This as closely as possible mimics the experimental data and setup of the DES-Y3 analysis. We also run a chain with this setup, but with the marginalisation of redshift uncertainties replaced by the Δ approach.