dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on an iterative exchange with stakeholders and consortium members and a literature review on digital agriculture tools, we identify and present underlying requirements based on which we developed the DAKIS. Subsequently, we present the DAKIS' structure and components. The DAKIS is a digital knowledge-based DSS that integrates remote and in situ sensors, AI algorithms, and online web platforms with publicly available real-time databases, farm economics planning modules, modelling of ESS and biodiversity, agent-based modelling, stakeholder-based stated demands, and sustainability impact assessment. We then highlight its principal novelty: the integration of diverse digital agriculture methods and technologies to provide decision support which simultaneously (i) incorporates ESS and biodiversity into farmers' decision-making processes; (ii) fosters communication and cooperation between farmers and other actors; and (iii) links multiple spatiotemporal scales and broader sustainability levels.
Development of the DAKIS architecture and proof of concept For the development of the DAKIS architecture, we used a design thinking approach, see for example Plattner et al. [22] , involving the consortium members to establish the overall conceptual and technical architecture, as well as the interfaces between its different components. That aimed to ensure the effective cooperation of diverse disciplines and the development of a conceptually and technically sound approach. To achieve this, in addition to monthly meetings within and across the project work packages, we held regular weekly or monthly meetings of thematic task forces (e. g. , management design, database design, decision-making processes, and indicators). To provide a proof of concept of the DAKIS, the tool is currently being tested in two agriculturally very different test regions in Brandenburg and Bavaria in Germany.
Therefore, it needs to consider a range of key pedoclimatic, economic, policyrelevant, and socio-cultural aspects that influence farmers' land use and management decisions. Further, it needs to provide impact assessments for a broad range of ESS and biodiversity outcomes related to proposed land-management options while capturing and integrating ESS and biodiversity provision datasets with sustainability demands from different stakeholders and land managers. Requirements regarding the temporal and spatial coverage of the DAKIS pointed towards the need to develop solutions addressing aims across multiple spatial scales and time horizons. Both ESS and biodiversity provision and demand need to be formulated at sub-field, field, farm and landscape scales in a spatially explicit manner. The DAKIS will facilitate site-specific Table  1  Requirements for the DAKIS according to the iterative process with stakeholders and consortium members.
To examine the variability of within-field ESS potentials, we subdivide the site into 64 rectangular sub-patches of 0. 5 ha. These are parallel to the permanent traffic lane with an edge length of a multiple of the maximum working width. First, we assess yield production potentials across these sub-patches using real multi-annual yield maps and a fuzzy k-means clustering algorithm  [57]. We then apply an erosion hotspot analysis, using site-specific remote sensing data, to the same area to assess erosion control potentials at the sub-patch level. Both datasets are integrated to quantify varying yield and erosion control potentials across sub-patches. 2. We then formulate the central underlying assumption that to minimize trade-offs between yield production and erosion control, grassland buffer establishment should be prioritized on sites with low yield potential and high erosion control potential.
Future uptake of the DAKIS In a perfect world, the demand placed by society on the provision of ESS and biodiversity would be satisfied by farmers with the help of the DAKIS. However, in reality, although the principles and tools presented here have great potential to support sustainable agricultural transformation and a move towards site-relevant and adaptive agro-ecological management solutions, implementing such approaches at scale requires strong buy-in not only from individual farm-level stakeholders but also from industry and policy players  [9, 70] ;  Ehlers et al. , 2022). Such developments involve systemic changes to farming and agri-food systems, e. g. , to open new avenues of producer-consumer interaction and value chain design  [69]  and to structurally address data privacy or data storage concerns  [67, 68].
Farm advisors can actively support overcoming tradeoffs and maximize synergies in the farm, but also landscape and regional planning contexts. From a practical perspective, this would also facilitate immersing into the complexity of the DAKIS system and its implications for farm management and reducing potential technical, financial, administrative or knowledge barriers to farmers themselves. Ultimately, the DAKIS proposes a novel framework through which digital agriculture may be harnessed to facilitate current farm management processes and transform the way farm actors use and integrate multiple flows of information about farming landscapes. To align tool development with this future vision, the DAKIS proposes an adaptable framework structure to manage complex data connections where the range of included datasets and land management options can easily be extended based on end-user targets, related information needs and data availability.
A principal novelty of the DAKIS is that it uses digital technologies to render possible the consideration of ESS, biodiversity, and sustainability into farmers' decision-making, providing a DSS through which farmers are informed and guided towards site-adapted small-scale multifunctional and diversified agriculture along self-defined avenues. To achieve this, it bundles and analyses a wide range of static and dynamic data from various sources delivered to the user via an interconnected spatiotemporally explicit approach, which assimilates the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. To encourage longterm participation and adoption of the DAKIS by important stakeholders, it maintains the scale-relevance of its outputs at the subfield and farm level while offering a learning system for an inclusive stakeholder exchange on sustainable development objectives.
Acknowledgements This work was made possible through funding from the  Digital Agriculture Knowledge and Information System (DAKIS)  Project (ID:  FKZ 031B0729A ), financed by the  German Federal Ministry of Education and Research (BMBF). Sincere thanks to  Amir Armaghan  for his amazing sketches on the DAKIS GUI, enabling us to approach the work from the user's perspective. We acknowledge the valuable contributions of  Stefan Zachaeus , Sebastian M€ oller and  Nils Niemann  on the design of the DAKIS back end. We thank the many other members of the DAKIS crew that one way or another contribute expertise and input to the development of the DAKIS.
The School-Work Alternation (SWA) programme was developed (under a European Commission call) to bridge the gaps and establish a well-tuned partnership between education and the job market. This work details the development of the AI4Citizen pilot, an AI software suite designed to support the SWA programme. The AI4Citizen pilot, developed within the H2020 AI4EU project, offers AI tools to automate and enhance the current SWA process. At the same time, the AI4Citizen pilot offers novel tools to support the complex problem of allocating student teams to internship programs, promoting collaborative learning and teamwork skills acquisition. Notably, the AI4Citizen pilot corresponds to a pipeline of AI tools, integrating existing and novel technologies. Our exhaustive empirical analysis confirms that the AI4Citizen pilot can alleviate the difficulties of current processes in the SWA, and therefore it is ready for real-world deployment.
In this context, in this paper, we illustrate the development of the AI4Citizen pilot, a software prototype designed as a solution to the shortcomings of the SWA programme. The pilot, developed in the context of the H2020 AI4EU project  [12] , provides AI tools to automate the SWA programme's current process while introducing new tools to facilitate team-based learning and the acquisition of teamwork skills. Notably, the AI4Citizen pilot involves pipelining a range of AI technologies, including (1) NLP algorithms to extract competencies and skills from students' curricula and companies' internship offers and to match them; (2) a chatbot to assist students in selecting internships; and (3) a novel algorithm to group students into teams and allocate them to internships. Specifically, the AI4Citizen pilot tries to make headway in supporting the SWA program with the following contributions: • An NLP-based tool to match students with internships.
Our chatbot understands 28 different types of students' intentions related to gathering information about the process and expressing and reviewing preferences. The NLP engine recognises the intents and triggers suitable skills to interact with the end users. These skills interact with a dedicated component interacting with the database minimising the need to exchange personal information outside the FBK perimeter. • An empirical evaluation of the chatbot usability. We conducted a user study to evaluate our study. Students from three different classes, 55 students in total, participated in the study. During the evaluation, each student was supposed to single out three internships (wishes) and understand the internship context. In the first class, 10% of the students did not manage to complete the evaluation due to a default which thereafter was fixed.
Indeed, frequently the descriptions of experiences and school curriculum are expressed in natural language, making it challenging to match requests and offers in an automated manner. To address this issue, we have defined and developed a tool that bridges the gap between the required and provided competencies when expressed as a free text description. To reach this goal, we use two existing resources, (i) the ESCO Ontology  [13]  and (ii) the FastText software  [16]. ESCO is the multilingual classification of European Skills, Competences, Qualifications and Occupations. It contains a taxonomy of 13,485 competencies and 13,485 jobs connected with relations. It has been developed as a part of the Europe 2020 strategy. It identifies and categorises skills, competencies, qualifications, and occupations relevant to the EU labour market and education and training.
It systematically shows the relationships between the different concepts described in 27 European languages. It has been recently adopted in various countries and regions to describe the professional context in the corresponding territories unambiguously. We use the ESCO ontology as a basis for the description of the job and internship offers as well as for characterising candidates' skills and experiences. The latter resource, FastText, is a state-of-the-art technology developed by Facebook, which computes the semantic similarity between two text spans. It is released with an opensource license and is available in over 100 languages. FastText is an evolution of word2vec  [17] , a word representation model where each word is an object in a vector space, and its position is optimised for the task of predicting the surrounding context.
We also enriched the gold entities with eight custom entities serving three different purposes: • to enable domain-specific interactions, this encompasses competencies (e. g. , related to the ECSO ontology), companies' names and cities contained in the internship database. While we initially did not foresee adding cities as entities, we realised that the related gold entities lack precision for small cities. Fortunately, these entities can be loaded thanks to the chatbot API automatically upon database modification; • to capture the format of specific identifiers; • to enable the students to refer to the chatbot artefacts (e. g. , to ask questions about the chatbot itself). In order to propose individual advice to students, the system should access or store information related to each student.
g. CPLEX  [14] , Gurobi  [41] ), GLPK  [42] , or SCIP  [43] ). Given sufficient time, an LP solver will return an optimal solution to the NOMTMT allocation problem. Notice that to solve the LP above, we need to precompute the competence affinity between each task and every possible size-compliant team, meaning that we need to precompute the optimal CAF (via solving an optimisation problem) for each such team,task pair. This is bound to lead to large linear programs as the number of agents and tasks grows.
Evaluation of competence and skill extraction This section aims to evaluate the extraction capabilities of the CSE tool introduced in Section 4. Recall that the CSE tool results from combining the ESCO Ontology  [13]  and FastText  [16] , allowing us to relate the texts taken from a CV with job offers by associating them to the competencies in the ESCO Ontology. To evaluate the CSE tool, we performed two different experiments. In both experiments, we used competencies, CVs, and job offers described in Italian. For the first experiment, we adopted the FastText similarity algorithm out-of-the-box: (i) first, the description of the competence in natural language is converted into a vector; (ii) similarly, vectors are also computed from all the competences present in ESCO, written in the same language of the required competence; (iii) the cosine between the vector in (i) and each of the vectors in (ii) is calculated, and the results are ranked.
Generally, the system performs well, and our experiments allowed us to understand which algorithm performs better. Most of the inaccuracies are due to the imbalance of the ESCO ontology: some branches are very detailed, while others are often poor or missing at all. Both the approaches (with and without TF-IDF) are easy to extend to any language, as long as both FastText and ESCO are available in that language. Since the pre-processing phase using TF-IDF outperforms Fastext alone, we use that algorithm in the AI4Citizen pilot. Notice that our algorithm is publicly available on Github  [18].
For example, the match between students and internships is done by a component hosted within the FBK network. Similarly, the matching algorithm only manipulates a set of preferences without access to students' names or other PII. The information exchanged by the components is clearly described by a set of public APIs avoiding direct access to the underlying database. Finally, commu-nication is done over secure communication channels with client authentication. For the usage of AI, we analysed the intent of the automation and the impact on the types of users -students and professors -of the solution. For students, our solution gathers the students' preferences by recommending a set of proposals based on the tagging done by the school and automated mapping to the job ontology, the fit between the individuals' offered skills and the internship required skills (provided as information).
Funding Open Access funding provided thanks to the  CRUE-CSIC  agreement with  Springer Nature. The research leading to these results received funding from project  AI4EU  ( H2020-825619 ). Athina Georgara  has received funding from  Generalitat de Catalunya  under Grant  2019DI17 ; and has received research support from company  Enzyme Advising Group. Furthermore, partial financial support was received from projects:  CI-SUSTAIN  ( PID2019-104156GB-I00 );  Crowd4SDG  ( H2020-872944 );  TAILOR  ( H2020-952215 );  VAE  ( TED2021-131295B-C31 ), funded by  MCIN/AEI /10. 13039/501100011033  and by the  "European Union NextGenerationEU/PRTR; VALAWAI (Horizon Europe  # 101070930 ); and  2021 SGR 00754.
S10). To ensure that the observed associations were not driven by somatic variation potentially introduced during iPSC generation, cell seeding or genome sequencing, we repeated our analysis restricting to only those variants that were previously observed in the gnomAD database  35  (106,590 of 122,256 variants). We recapitulate all observed associations (significant after Bonferroni correction for multiple testing and with suggestive evidence) with concordant effect size and statistical significance (p-value) (Fig. S11 ). Taken together, our analyses indicated that, using our dataset, we could successfully identify associations between rare coding variants and several morphological traits.
These findings suggest this framework could be applied to relevant cells and tissues such as iPSC-derived differentiated cells, post-mortem brain samples or excisable somatic cells. Second, though our study provides the largest (to our knowledge) image-based iPSC phenotyping dataset, we are still underpowered to detect a significant number of high confidence common variant cmQTLs. Future efforts may require cross-institutional collaborations to adequately scale in vitro sample sizes for common variant cmQTL identification. This approach holds significant promise for future studies leveraging human-derived, diseaserelevant cell types for modeling the impact of genetic variation on cellular function. The use of imaging to capture phenotypes is particularly attractive in experimental designs for several reasons, such as the low cost per cell for imaging, and the ease of processing data and preparation of the cells or tissues as compared to the generation of other molecular data such as RNA-sequencing or epigenomic assays  51.
Figures Fig 1. Study Overview iPSC lines from 297 donors were expanded, quality-control checked and then subject to both high-throughput imaging with Cell Painting and 30X whole-genome sequencing. Overall, we imaged 5. 1x10 6 cells across all donors and quantified 3,418 morphological traits per cell using CellProfiler software. We inferred genetic variants from the WGS data and investigated whether individual morphological traits associated with both rare and common variation.
Material and iPSC generation Our dataset comprised 297 donors from the iPSC repository of California Institute for Regenerative Medicine (CIRM) (Supplementary Table  1 ). Either B cells or Fibroblasts were taken from each donor from which iPSC lines were generated using non-integrating episomal vectors. Cells were cultured in StemFlex (ThermoFisher; cat#A334901) culture media and passaged for expansion with 1mM EDTA (Gibco; cat#15575020) and 10uM Y-27632 (StemcellTech; cat#72304). Each iPSC sample underwent Global Screening Array (GSA) for karyotype analysis to ensure chromosomal integrity, as well as 30X whole-genome sequencing to determine genome-wide variants for each donor. Each iPSC line was cultured between passages 12 and 15 before use in the experiment.
WGS data quality control for common variant association analysis The QC-ed WGS VCF file was processed using plink v1. 90b3 to remove sex chromosomes, multi-allelic variants, variants with duplicated positions, and small insertions and deletions larger than 5bp. Of 38,239,223 variants loaded from the VCF file, 33,348,914 passed these filters. Donor-level genotype missingness rates were checked to exclude donors with genotype missingness rates > 10%. All 297 individuals passed this filter. Finally, variants with minor allele frequency (MAF) < 5%, missingness > 5%, and Hardy-Weinberg equilibrium p-value < 10 -5 were excluded, following which, 7,020,633 remained for common variant association analysis.
CRISPRi sgRNA design, cloning, and virus production To functionally validate the rare-variant burden associations, we designed sgRNAs targeting the transcriptional start site (TSS) for each gene using CRISPick software (Doench, 2016, Sanson,    2018). sgRNA oligonucleotides were cloned into the CROPseq vector using a Golden Gate cloning protocol (Juong, 2017). To validate sequence insertion, DNA plasmids were sequenced by a 3rd party provider. Plasmids with successful insertion were packaged for lentivirus generation using TransIT-293 reagent (Mirus Bio cat#: MIR 2704) and packaging plasmids VSV-G and DVPR (need to confirm these). HEK239T (ATCC cat#: CRL-3216) cells were transfected with sgRNA packaging plasmid and incubated for 48hrs. HEK239T media supernatant was collected, and lentivirus was concentrated using LENTI-X concentrator (Takara) per manufacturer's instructions.
Acknowledgements We thank members of the  Nehme ,  Carpenter-Singh , and  Raychaudhuri  labs for insightful discussions and critical reading of the manuscript, and  Anna Neumann  for project management. This work was supported by a  Broad Institute Variant to Function  ( V2F) Initiative  grant, the  National Institutes of Health  ( NIMH U01 MH115727  to RN /KE/SAM  and  NIGMS   MIRA R35 GM122547  to AEC), the  Chan Zuckerberg Initiative DAF , an advised fund of the  Silicon Valley Community Foundation  (grant number  2020-225720  to BAC), as well as the  Stanley Center for Psychiatric Research at the Broad Institute. The authors also gratefully acknowledge the use of the  PerkinElmer Opera Phenix High -Content/High-Throughput imaging system at the  Broad Institute , funded by the  S10  Grant  NIH OD-026839-01.
Data Availability Images and preprocessed profiles that are augmented with gene and compound annotation are available in the Cell Painting Gallery on the Registry of Open Data on AWS (https://registry. opendata. aws/cellpainting-gallery/) as dataset `cpg0022-cmqtl` at no cost and no need for registration. Whole genome sequencing for cell lines used in this study are hosted. CC-BY 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted January 9, 2023. ; https://doi. org/10. 1101/2023. 01. 09. 522731 doi: bioRxiv preprint on Terra https://app. terra. bio/#workspaces/anvildatastorage/AnVIL_NIMH_Broad_ConvergentNeuro_McCarroll_Eggan_CIRM_GRU_WGS Code Availability Source code to reproduce and build upon the presented results is available at https://github.
a b s t r a c t At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. In the context of the "Micro Reseau Mafate" research project, financed by the European Regional Development Fund, the PIMENT Laboratory and the local electrification union SIDELEC aim to develop microgrid stations in this isolated area. More than 300 homes are concerned by this alternative solution that these microgrids represent. Presented data come from an experimental microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries. Local data are collected to simulate electrical systems and determine the optimal working point of energy distribution. These data concern the meteorological conditions, the electrical performance of devices and the inhabitants' usage of appliances.
Value of the Data The repository contains recordings of the meteorological data, photovoltaic production, battery current and loads of electrical appliances of the 3 homes. These results have value because: • Data are from the first meteo-station which has been installed at "Roche Plate". These weather data are used to define solar resources for designing the other microgrids currently in progress in Mafate. These data can also be used to develop forecast models of solar resources for tropical climates. • The data are related to the measurements of the physical parameters of the microgrid in operation. Data are at a fine scale and can be useful to validate models of various operating systems involved in a microgrid: photovoltaic panels, battery cells, an inverter, a controller, etc… • The dataset is related to real-life usage of electricity by users.