dataesr/scientific-paragraphs-categorization huggingface dataset:
This has two significant facets: firstly, missing data management (i. e. , datasets are neither findable because of missing community standard metadata and taxonomies) and interoperable data (i. e. , missing standards for data formats); secondly, data owners having a negative perception of sharing data. It is increasingly clear that we need to introduce information science and data science elements to deal with the data challenge. The former is essential for data management to organise and make data ready for data science exploitation. The latter enables the digitalisation process, i. e. , transforming data into information and then into value to find innovative solutions and business models, e. g. , to cut costs by better planning, optimising processes to increase operational efficiency and by reducing risk to optimise investments.
However, to be exploited, data must be available to potential users. To do so, different paradigms have been proposed such as open or Findable, Accessible, Interoperable and Reusable (FAIR) data which will be discussed in the next section. In any case, data for each topic in a specific field must also be findable; for this, a topic taxonomy for energyrelated fields is necessary for two reasons: to standardise the nomenclature, supporting a common understanding amongst the actors, and to classify existing and future data available in this field. In this paper, we address the information science issue by creating metadata and a taxonomy of the topics of photovoltaics and concentrated solar power based on previous work carried out by the wind energy community. We adopted expert elicitation where a group of experts in different renewable technologies and for cross-cutting fields such as Life Cycle Assessment (LCA) and the European Union (EU) taxonomy for sustainable activities have been gathered to propose a consistent and detailed taxonomy for renewable energy-related data.
e. , a series of information, e. g. , who, what, when, where, how etc. to put data in a context. Furthermore, metadata should be machine-actionable allowing for searchability and usability by machines. With metadata, data are preserved for future reuse. These can be general or discipline-specific, with the first defined, for example, by the Dublin Core (DC)  [3]  whilst the second must be defined by domain experts. Within the information included in metadata is the position of the dataset in a relevant taxonomy, which is a method to structure the knowledge related to a discipline into topics. An example is the famous biological classification or the most recent EU taxonomy for sustainable activities  [4]. In this work, the most common hierarchical taxonomy approach has been used. Regarding data availability, it is observed that datasets about renewable energy resources are now largely accessible thanks to long-term work carried out at independent research centres and large multinational organisations.
This guarantees the reliability of the taxonomies. Details of the work are presented in  [9]. This work is also enhanced by the production of a metadata schema and the design and demonstration of the metadata catalogue Share-Wind. The metadata schema includes the list of general Dublin Core metadata completed by wind energy domain-specific metadata and related taxonomies/vocabularies. Data owners can register the metadata of available datasets to populate the data catalogue, and data users can search for needed data. The platform Share-Wind is in the process of being transferred to the domain http://share-wind. net (accessed on 29 October 2021). Another significant activity on this topic has been carried out at NREL  [10]  and IEA  [11]  levels. In the first case, a taxonomy has been developed for studying the cost structure of wind energy to identify potential cost reduction sources.
A comprehensive view of the topics in LCA can be found in  [29]  and aspects related to uncertainty are detailed in  [30]. The Share-Wind initiative started as a data catalogue for wind energy-related datasets and evolved into a fully searchable metadata catalogue  [31]. On a general level, relevant work is the above-mentioned EU taxonomy for sustainable activities  [4] , developed to direct investments towards sustainable projects and clarify to the industry which type of investments can be considered sustainable. This taxonomy covers several topics affecting the level of sustainability of a system, including electricity production from renewable sources, which is the scope of this work. For each of them, information is given on the type of effect that investments have on improving the impact on the environment, whether their nature is permanent or transitional, direct or enabling, etc.
This is shown in Table  5. Table  5. Hierarchy for Component-related topics in the Production of Electricity from Solar PV. • Components o Plant ▪ Meter ▪ Inverter ▪ Grid • Cabling • Protection • Grounding ▪ Monitoring and Control o Conversion Technology ▪ Cell • Technology • Characterisation ▪ Module • Architecture • Characterisation The third facet, based on the assumption described in Section 2. 3. 4, groups together aspects related to the life cycle of the plant, from the manufacture of the cells and other equipment to the decommissioning of the plant, passing through the construction phase and operation and maintenance, where data related to the production and faults are present. It is based on the assumptions described in Section 2. 3. 4. This is shown in Table  6.
Discussion and Conclusions The work presented in this manuscript represents a first attempt to classify information related to renewable energy technologies. It is believed that this will help to accelerate research and innovation in the field of energy digitalisation, in two different ways. On one side, such taxonomy can be applied to classify existing and future datasets, facilitating their diffusion and reuse. On the other hand, it can help to identify the availability of existing datasets, or the areas where available datasets are less common. The analysis, limited to selected technologies for electricity production from renewable resources, identified more than 400 unique terms. They are divided into topics specific to the three technologies chosen: photovoltaics, concentrated solar power and wind power, and six categories for metadata: activity, instrument, material, models, variables and external conditions.
• A contribution to the development of a FAIR data ecosystem for renewable energyrelated data. The possibility to extend the approach to other renewable energy generation and support technologies is considered fundamental for this work. It also represents a step further from previous attempts which were focused on individual technologies (wind, PV) but would not share datasets for a similar topic. An example can be resource estimation which is based on meteorological and climatological assessments for many renewable technologies, but treated differently in technology-specific taxonomies. Other examples are aspects related to grid connection, planning and operation or LCA. The standardisation allowed with the approach proposed is expected to alleviate two main problems: (1) it can facilitate communication and hence research and development, and (2) facilitate system interoperability on the industrial level.
Topic nomenclature is in fact currently suffering from the use of different definitions on the company or sector level. The development of FAIR data environment is considered necessary to facilitate and accelerate research and development. In particular, it can improve research relevance by preventing analysis from being carried out on small and partial datasets and can facilitate study replicability. These two problems are among the main ones plaguing the current research community and decisive actions must be taken in the coming years. Finally, it is possible to mention further research areas opened by this work that can be summarised as follows: (1) the extension to other renewable and sustainable energy technologies included in  [4] , which should be carried out in parallel with (2) extending the harmonisation and deepening the detail for ancillary aspects such as grid connection and support functions, (3) extending the work to software and (4) using the taxonomy and metadata to structure a catalogue for facilitating user searches of classified data and finally (5) developing automated tools to verify the quality of the datasets proposed and suggesting the most relevant tags.
Spatial Branch-and-Bound. Other Global Optimization approaches for the ACOPF problem follow Spatial Branch-and-Bound schemes  [4]. To obtain a lower bound at each node of the exploration tree, these algorithms may use a Second-Order Cone Programming (SOCP) relaxation  [21] , a Quadratically Constrained Programming (QCP) relaxation  [12]  or a Semidefinite Programming (SDP) relaxation  [7]. Piecewise convex relaxations. Rather than implementing a Spatial Branch-and-Bound algorithm from scratch, an alternative is to encode branching decisions via binary variables and use an off-the-shelf Mixed-Integer Programming solver. This leads to piecewise convex relaxations, which can be iteratively refined. This is the approach followed in  [25] , leading to convex Mixed-Integer Quadratically Constrained Programming (MIQCP) problems.
Experimental setting For all experiments, we use a 64-bit Ubuntu computer with 32 Intel(R) Xeon(R) CPU E5-2620 v4 @ 2. 10GHz and 64 GB RAM. Along our algorithm, we use the commercial solvers MOSEK  [1]  and CPLEX  [18]  called through their Python APIs, as well as the academic solver IPOPT  [33]  called through the Pyomo interface  [16]. We compute the tree decomposition with the approximate minimum degree (AMD) ordering routine of the chompack package. We consider a relative optimality gap of 0. 01% for global optimality (GOPT) and use the parameters (N 1 , N 2 , ) = (4, 4, 10 -6 ). The FBBT and OBBT procedures are applied for all variables a maximum of 4 times, and with a time limit of 10 hours (TL 1 ). After each pass of FBBT and OBBT, we apply the Floyd-Warshall algorithm and we check whether the gap of the tightened conic relaxation reaches the target optimality gap.
If the maximum number of Bound Tightening iterations or time limit is reached, we enter the MILP iterative scheme with a time limit of 5 hours (TL 2 ). Our code is available at github. com/aoustry/SDP-MILP4OPF. This study focuses on the network instances from the IEEE PES PGLib AC-OPF v21. 07 library  [2]  with less than 500 buses. As shown in Table  2 , the instances of this benchmark are split in three categories depending on their characteristics: Typical Operating Condition (TYP) instances correspond to a reference scenario, Congested Operating Condition (API) correspond to situations with greater Power Demands, and Small Angle Condition (SAD) correspond to tighter constraints for the Phase Angle Difference. We compare our approach with the standard SOCP and SDP relaxations  [28] , and with two other Global Optimization approaches  [13, 32].
We performed these comparative experiments on the same PGLib v21. 07 instances, with the same hardware, the same time limit (TL 1 ) as our OBBT algorithm, and the same relative optimality gap tolerance. The concurrent approach from  [32]  is an OBBT algorithm based on a strengthened QC relaxation. We ran the Julia implementation of this algorithm provided in the PowerModels. jl package  [8]. The QC relaxations are solved with IPOPT. The concurrent approach from  [13]  consists of an OBBT algorithm, based on a Determinant SDP relaxation strengthened with RLT constraints. We ran a C++ implementation of this algorithm, that is based on the Mathematical Modeling Language Gravity  [17] , and is available at the link indicated in  [13] ; The corresponding relaxations are also solved with IPOPT. We point out that these competing approaches  [13, 32]  solely rely on open-source tools, whereas our implementation uses commercial solvers.
Methods In the absence of fundamental research on the Moroccan AECOO situation, it is improving and renovating. And the presence of weak scienti c production according to the famous databases (Scopus, Web of Science, Google Scholar) on subjects related to the improvement of the performance and productivity of the economic sector. Hence the idea to conduct this fundamental study. So, to act better and propose the right solutions to an unfavourable and paradoxical situation such as that of the AECOO industry, it was necessary rst of all to analyse the existing situation and decipher all the dimensions. We adopted the Five W's and How's (5W1H) approach, derived from quality management and the Lean philosophy. This constructive critical analysis tool is based on systematic questions  [7]. Turning the problem in all directions results in the dimensions of the Moroccan construction industry that require study and clari cation.
21, 22 he organisation of this article is as follows; in Section 2, we motivate the problem by considering two example data sets; in both cases, a literature search on clinicaltrials. gov and PubMed yielded a number of phase I studies investigating a novel drug at a range of dose levels. In Section 3 we introduce the statistical methods for MTD estimation and meta-analysis used in the following. In Section 4, we investigate the proposed methods' performance using Monte Carlo simulations considering the combined estimate as well as shrinkage and prediction. In Section 5, we analyze the example data, and Section 6 finally closes with some conclusions.
6. 2. Summative evaluation 6. 2. 1. Participants A total of 10 people participated in this evaluation, which constitutes a good minimum sample size for comparing applications in summative testing  [? ]. At the beginning of the experiment, we asked the participants to fill an initial questionnaire with some personal data (without identifying them by name or asking them for sensitive data) and some data about their technical knowledge. Regarding their nationality, 60% of them were Argentineans while the remaining were from Ecuador. Their ages ranged from 21 to 49, with an average age of 34 years old and a standard deviation of 8 years. Only one of the participants was a female; only one participant was ambidextrous.
e. task completion and satisfaction using some questionnaires, to obtain valuable feedback to improve our tool. This allowed us to fix bugs and a few usability issues related to the tools, which resulted in a second release of the tools that were then used for the second run of the experiment. However, although we did not obtain performance data from the preliminary study, what we can say is that a big majority of the participants were able to create the search services and use them. Between the two studies (although with different tool versions) 27 participants could use the tools for both tasks: create and trigger search services with ANDES. This is a very important result given that creating the search service is a core task in our approach. Regarding the summative experiment, we collected the time required by the participants to perform the intended tasks.
The applicability of the tool has two dimensions: i) compatibility of the ANDES extension with Web browsers, ii) compatibility with Web sites search engines. Regarding the first one, our tool is based on the up-to-date guidelines for developing this kind of software, which makes it usable in Chrome (Google Chrome or Chromium) and Mozilla Firefox. Regarding the second one, we have defined a metamodel that covers the most common aspects of the user interfaces that Web sites offer for their search engines, and we have detected several strategies for triggering the search and obtaining the results. Although.
Introduction As of today, there are several public repositories for genetic and genomic variation data. However, most of these repositories are exclusive to humans and do not include other organisms  (Cezard et al. , 2021) , such as dbSNP  (Sherry et al. , 2001) , dbGaP  (Mailman et al. , 2007)  and dbVar  (Lappalainen et al. , 2013). There are two main resources for non-human variation data: The European Variation Archive (EVA)  (Cezard et al. , 2021) , hosted by EMBL-EBI, and the Genome Variation Map (GVM)  (Song et al. , 2018) , hosted by CNCB-NGDC. Submitting datasets to these two repositories works very similarly, but we will focus on the submission of genotyping datasets to EVA. Data and metadata are submitted to a File Transfer Protocol (FTP) file server and, after a quality check, are added to the database and displayed on their respective websites or kept hidden until a user-specified release date.
Data are only checked for a few critical points: first, the VCF file must comply with the Variant Call Format (VCF)  (Danecek et al. , 2011)  specifications, second, the genome assembly used as reference must be registered with one of the databases of the International Nucleotide Sequence Database Collaboration (INSDC)  (Cochrane et al. , 2011 ), i. e. , GenBank (Benson et al. , 2013) , the European Nucleotide Archive (ENA)  (Leinonen et al. , 2011)  or the DNA Data Bank of Japan (DDBJ)  (Mashima et al. , 2017) , respectively, and an accession number is available, and third, the VCF file must contain either allele frequencies and/or genotype information. When a data submission is made to the EVA, samples are automatically registered in the associated BioSamples database  (Courtot et al. , 2022) , unless this has been explicitly done previously by the data submitter.
Such automatically created samples possess only the minimum necessary attributes (name, domain, release date) and no other descriptive metadata. If pre-registering samples in BioSamples, metadata can be specified as key-value pairs. For some specific use cases, there are already predefined checklists that list which metadata should be supplied on sample registration, against which the metadata can be validated. Additional information, which is not yet available in a defined attribute, can also be submitted under a free text key. We recommend the manual registration of samples at BioSamples as this gives the greatest flexibility when editing and adding information. The description of these samples must take into account plant specificities to fully enable findability and interoperability with reference databases such as  EURISCO (Weise et al.
, 2017)  or datasets like phenotyping experiments to allow for complete reusability. The biggest challenge is probably the accurate identification of biological material, including varieties, lines, RILs, and crosses. This has been standardised by the MCPD  (Alercia et al. , 2015)  and MIAPPE  (Papoutsoglou et al. , 2020)  data standards, which introduce an international ID mechanism based on DOI and handling of genealogy and pedigree. Indeed, experience has shown that the mere indication of a cultivar name is not sufficient and introduces a lot of ambiguities regarding the material that has actually been genotyped. One hurdle in enforcing a uniform format specification for variation data in plant science is the fact that there are some databases that offer their own plant-specific variation data.
These databases often belong to either the project-specific or the aggregation database classes (which are often organism-specific). The former do not change after the project lifetime, while the latter summarise the results of several studies in a uniform way. Probably the best known project-specific database is the GMI-MPI of the 1001 Genomes Consortium  (Alonso-Blanco et al. , 2016). An example for an aggregation database would be https://jbrowse. arabidopsis. org where a large number of Arabidopsis Thaliana VCF records from different studies is stored. In general, it should be possible to adapt metadata to new guidelines for project-specific databases, whereas it would be much more difficult to obtain this information for aggregation databases. Another useful resource for the analysis of plant variation data is Ensembl Plants  (Howe et al.
, 2020). This database, also hosted by EMBL-EBI, is a platform for displaying and visualising plant genomes. If the reference genome assembly associated with the data submitted to EVA is supported in Ensembl, then it is possible to display genetic variants in their genomic context in the Ensembl browser, each linked to its sample. Data submitters should contact Ensembl helpdesk to request it. VCFs in EVA should be available as browsable files, as seen for example in soybean.
REVISED Amendments from Version 1 In version 2 of this article, we have revised the Abstract and added larger sections to both the Introduction and the Conclusion. In particular, we have addressed the reviewers' comments on the introduction of the VCF recommendation in the broader community as well as various aspects of the FAIRness of the adapted metadata. Throughout the article, we have adjusted and clarified some unclear passages and taken greater care in the correct designation of pronouns and genderneutral language. We have also submitted a sample dataset to EVA that meets the VCF metadata specifications in this article and added guidance in the FAIR Cookbook on submitting genomic and genotypic data to EMBL-EBI. Any further responses from the reviewers can be found at the end of the article Lessons learned from studies on plant phenotyping and its application to metadata information in genotyping The standardisation of plant variation data is still in its infancy.
Meta-information lines are considered optional; however, they need to be well-formed if present. This means that all structured lines that have their value enclosed within "<>" require an ID which must be unique within their type (Figure  1 ). A critical aspect of VCF specifications is that sample naming within the VCF file does not follow any standard specifications, i. e. users can name their samples without reference to any real biological material. Even worse, phenotyping and genotyping data from the same experimental setup often use different sample identifiers even when the same biological material has been used, which makes it difficult to reconstruct later which datasets were derived from a common sample. To be able to represent such relationships, descriptive metadata is required that relates these different sample identifiers to each other.
In response to the points discussed previously, we propose a minimal list of metadata fields, recommend an identifier schema and guidelines for vocabulary and data format within a VCF file. Our suggestions are divided into recommended and optional changes. Although, we are primarily addressing data submissions to the EMBL-EBI repositories BioSamples and EVA (and implicitly ENA through the submission of sequence information), subsequent formatting guidelines should be applied regardless of the specific deposition repository and should also be considered when designing databases and APIs. In our view, these additional fields should be required for a valid VCF: One meta-information line, ##fileformat, is obligatory in VCF. We also recommend using the additional lines ##filedate, ##bioinformatics_source, ##reference_ac, ##reference_url, ##contig and ##SAMPLE.
##bioinformatics_source=url This is ideally specified as the DOI of a publication, or more generally as URL/URI (like a public repository for the scripts and parameters used). Examples: 1) Description of a GBS experiment in barley and subsequent read alignment and variant calling using a bioinformatics analysis pipeline consisting of cutadapt, BWA-MEM, SAMtools, NovoSort, Picard, BCFtools and seqArray. ##bioinformatics_source="doi. org/10. 1038/s41588-018-0266-x" 2) Modified version of Tassel4 (v. 4. 3. 7) for running the Tassel-GBS pipeline modified for polyploid species with high read depths used in  (Pereira et al. , 2018). ##bioinformatics_source="github. com/gramarga/tassel4-poly".