dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it is not robust, as its effectiveness often remains very low, despite making the most of schema knowledge. In contrast, the schema-agnostic workflow consistently achieves excellent effectiveness after fine-tuning and allows for a default , parameter-free configuration. This is impossible with the join-based workflow, as it depends on the schema of the dataset at hand. Baseline Systems. We now compare JedAI with the stateof-the-art in the field, Magellan  [3]  and DeepMatcher  [66]. Neither of them is applicable to Dirty ER, as both require two duplicate-free datasets as input. For the Clean-Clean ER datasets D c1 ,. , D c6 , we consider the top performance that is reported in  [66]  among all configurations and dataset versions (we couldn't reproduce it ourselves, due to the lack of necessary details and the human-in-the-loop approach of both systems).
We couldn't apply either system to D c8 , due to the extreme schema heterogeneity, but we were able to apply both of them to D c7. The resulting performance appears in Tables 4(e) and (f). We observe that for D c1 , D c3 and D c4 , at least one of the baseline systems outperforms the best configuration of JedAI's budget-and schema-agnostic workflow to a significant extent. In the last two cases, though, the F-Measure remains below 0. 8. For the remaining datasets, JedAI outperforms them to a lesser or a greater extent. This means that there is no clear winner among the three systems in terms of effectiveness (their overall difference is statistically insignificant). In terms of time efficiency, though, JedAI is much faster, since Magellan and DeepMatcher spend a considerable time for training their matching models -after performing Blocking with the help of an expert and after labelling a considerable number of comparisons  [66] , two operations that are quite time consuming.
For D c7 , for instance, they were trained over 16,800 pairs of entities (1:10 match/non-match), with Magellan requiring 21 seconds for training and DeepMatcher taking ∼40 minutes per epoch, i. e. , >3. 5 hours in total for 10 epochs. Scalability Analysis. To examine JedAI's scalability, we applied the default budget-and schema-agnostic (blockbased) end-to-end workflow to the seven synthetic Dirty ER datasets in Table  1. We also applied a default matching rule, JaccarS im(all tokens 1, all tokens 2) > 0. 4, executed by PPJoin and followed by Connected Components with the same similarity threshold (no other rule achieved reasonable effectiveness across all datasets). The resulting performance appears in Figure  7. In the legends, the prefixes B and J indicate the block-and the join-based workflow, respectively.
In Figure  7 (a), we observe that the blocking-based workflow excels in recall, which consistently exceeds 95%. Precision fluctuates between 84% and 94% and F-Measure between 96% and 89%. The larger the dataset is, the lower both measures get, since the ER task becomes harder -the candidate matches increase quadratically, while the duplicates increase linearly. In contrast, the join-based workflow consistently achieves perfect precision, while recall and F-Measure remain practically stable at 60% and 75%, respectively. These patterns verify the capa-bilities of the schema-agnostic workflow and the limitations of the schema-based one. The corresponding running times appear in Figure  7 (b). We have considered both the serial execution of each pipeline, which uses a single core, as well as the parallel, Spark-based execution, which employs all available cores of our server (i.
e. , 32 executors). We observe that most approaches exhibit similar run-times for the smaller datasets, requiring just ∼3 minutes for processing D 300K. The only exception is the serialized schemabased workflow, whose run-time increases quadratically with the input data size, due to the low similarity threshold it employs (the similarity join techniques like PPJoin are crafted for Jaccard thresholds higher than 0. 7  [39, 38] ). We can conclude that the overhead of Apache Spark does not pay-off for limited workloads. For the larger datasets, though, the parallel implementations take the lead, involving significantly lower runtimes. In particular, the parallel schema-agnostic implementation scales sublinearly with the increase in input data size, unlike the serial implementation, which scales superlinearly.
The linear scalability is demonstrated in Figure  7 (c), which depicts speedup s(n) = n min × RT (n min )/RT (n) as we vary the number of cores (i. e. , executors) in n ∈ {2, 4, 8, 16} when processing the two synthetic largest datasets, D 1M and D 2M. We observe that the speedup is very close to the ideal, linear one in all cases, demonstrating the high scalability of JedAI's Sparkbased implementation. For both workflows, the speedup is slightly larger for D 2M than for D 1M , which implies that it is affected by the size of the workload. Budget-awareness Experiments. We now evaluate the benefits of budget-aware end-to-end workflows. To this end, we estimate progressive recall, i. e. , the rate at which recall increases (on the vertical axis) as the number of executed comparisons increases (on the horizontal axis).
The larger the resulting area under curve (AUC), the more duplicates are detected early on and the better is the progressive functionality. We applied Progressive Global Top Comparisons to the blocks produced by the schema-agnostic workflow that is used in Section 9, i. e. , Token Blocking, Block Purging, Block Filtering and CNP. This approach essentially orders all comparisons in the final set of blocks in decreasing Jaccard weight. Its performance across all real datasets appears in Figure  8(a). The performance of the corresponding batch process, which executes the pairwise comparisons in arbitrary order appears in Figure  8(b). As the AUC suggests, the budget-aware workflow consistently outperforms its budget-agnostic counterpart to a large extent. Similar results are obtained when comparing the schema-based budget-aware workflow, which relies on the top-k simlarity join algorithm, with its budget-agnostic counterpart, with relies on token-based similarity joins executed by PPJoin (see  Section 9).
The only exceptions are the smallest datasets for Clean-Clean and Dirty ER, i. e. , D c1 and D cora , where the budgetagnostic workflow exhibits a performance equivalent to the budget-aware one, despite not using prioritization. The reason is that in both cases, the precision of the final set of blocks (PQ in Table  4 ) is so high that most comparisons involve duplicates without the need to prioritize them. Another reason is that D cora involves very large equivalence clusters so the transitive closure raises recall to a significant extent even with a small number of identified duplicates. Overall, the outcomes demonstrate that progressive recall increases significantly faster for the latter ones, as we execute more comparisons. This applies to both the schema-agnostic and schema-based end-to-end workflows.
However, few of these open code systems fulfill the second challenge  [67] , which requires them to process data of any structuredness. In fact, we can distinguish these systems into two broad categories according to the data structuredness they handle. The first category includes the open-source tools that are crafted for structured data, namely Magellan  [3] , Dedupe  [68] , DuDe  [69] , Febrl  [65] , FRIL  [70] , OYSTER  [71] , Record Linkage  [72]  and FAMER  [32]. All of them apply a budget-agnostic, schema-based end-to-end workflow that typically consists of two steps: Blocking and Matching. For Blocking, each tool provides few custom or established methods, except for Febrl, which offers the schema-based implementation of the main hash-and similarity-based techniques. For Matching, each tool provides various similarity measures, with Magellan offering the main similarity join techniques, too, to accelerate the execution of the matching rules it has learned.
Finally, JedAI offers a learning-free functionality that can operate with default configurations, independently of a ground-truth. It needs the ground-truth only for fine-tuning the parameters of its end-to-end pipelines and for benchmarking purposes. In contrast, the learning-based functionality of systems like Magellan, Silk and LIMES requires a labelled dataset for its supervised operation. Without such a dataset, they cannot learn any blocking or matching model. Note that a labelled dataset is fundamentally different from the ground-truth: the latter simply comprises positive instances, i. e. , the existing pairs of matches, while the former should also include a carefully selected sample of negative instances (i. e. , non-matching entities). The relative number and representativity of positive and negative instances affects significantly the performance of the learned model.
This is especially true for the recent crowd-sourced systems like Corleone  [81]  and Falcon  [82]  as well as human-in-the-loop sys-tems SystemER  [5]. This is not the case, though, with the learning-free approaches, which merely require users to finetune their generic (i. e. , domain and/or dataset-agnostic) pa-rameters. Generic supervised features are only employed by Deep Learning-based approaches, i. e. , DeepER  [83]  and Deep-Matcher  [66] , which are based on embeddings that are also supported by JedAI (see Section 4. 5). Overall, JedAI addresses successfully the four main challenges in building ER systems  [3] : it requires no coding from its users, it provides guidelines for creating effective solutions, it covers the entire end-to-end pipeline and it exploits a wide range of techniques.
Technical characteristics of the real and synthetic datasets for Dirty ER. |E| stands for the number of entity profiles, NVP for the total number of name-value pairs in the dataset, |N| for the number of distinct attributes, | p| for the average profile size (in terms of name-value pairs), |D(E)| for the number of duplicate pairs, and ||E|| for the comparisons executed by the brute-force approach. /NVP 2 1,130/7,519 2,568/2,308 5,302/9,110 10,464/9,162 14,143/1. 1•10 5 10,064/2•10 5 1. 6•10 5 /8. 2•10 5 1. 69•10 7 /3. 50•10 7 |E 1 |/|E 2 |.
62 10. 62 |D(E)| 17,184 299 8,705 43,071 85,497 172,403 257,034 857,538 1,716,102 ||E|| 8. 38•10 5 4. 77•10 7 5. 00•10 7 1. 25•10 9 5. 00•10 9 2. 00•10 10 4. 50•10 10 5. 00•10 11 2. 00•10 12 Type Real Real Synthetic Synthetic Synthetic Synthetic Synthetic Synthetic Synthetic D c1 D c2 D c3 D c4 D c5 D c6 D c7 D c8 Dataset 1 Rest. 1 Abt Amazon DBLP Walmart DBLP DBPedia DBPedia 3. 0rc Dataset 2 Rest. 2 Buy Google Pr. ACM Amazon Scholar IMDB DBPedia 3. 4 NVP 1 339/2,256 1,076/1,076 1,354/3,039 2,616/2,294 2,554/22,074 2,516/61,353 27,615/23,182 1. 19•10 6 /2. 16•10 6 |N 1 |/|N 2 | 7/7 3/3 4/4 4/4 6/6 4/4 4/7 30,688/52,489 | p1 |/| p2 | 3. 33/3. 33 2. 39/2. 14 3. 92/3. 00 3. 99/4. 00 5. 54/5. 18 3. 23/3. 26 5. 63/35. 20 14. 19/16. 18 |D(E 1 ∩ E 2 )| 89 1,076 1,104 2,224 853 2,308 22,863 892,579 ||E 1 × E 2 || 7.
r [76] CSV, JSON, XML LB No Java - C-C, D × (b) Systems for semi-structured data JedAI CSV, RDF/XML, RDF/HDT, LF Yes Java Apache Spark C-C, D RDF/JSON, OWL, Database (mySQL, PostgreSQL), SPARQL endpoint, Java serialized object Minoan-ER [80] RDF LF No Java Apache Spark C-C, D × (c) Systems for structured and semi-structured data.
g. , a similar pattern is observed in palaeontology), even if open data sharing has been acknowledged by the community for more than 15 years  [19]. The two main options for geochemists are EarthArXiv, a free preprint service for the Earth sciences initially powered by OSF Preprints, and ESSOAr, developed in a joint initiative by the American Geophysical Union with financial support from Wiley. Moreover, geochemists who have published in the many journals of the European Geosciences Union have already become accustomed to such openness and are posting their work prior to peer-review as a discussion on the Copernicus platform  [20]. Attempts to close this gap between the inherent benefits of preprints and their relatively low uptake by research communities have concentrated primarily on making preprints more citable, for instance by having improved metadata, permanent identifiers (DOIs), and even the look and feel of a conventional and historical journal article (e.
It is reasonable that a "one size fits all" model for preprints would never work for the entire scientific community and that geochemists need to develop and sustain their own model. The community should really be developing this, along with new standards for linking their datasets in the spirit of the FAIR (Findable, Accessible, Interoperable, Reusable) model  [24] , and the usage of open standards, licensing, and free and open source Software. In the near future, given the increase in the use and profile of preprint servers and alternative publishing platforms, such as F1000 Research, it will be necessary to identify how many relevant platforms exist, to describe their scientific scope (i. e. , covered disciplines), and, similar to the way that researchers evaluate the aims and scope of journals, to compare their characteristics and policies.
In the vast usage of public software sharing platforms (e. g. , Github, http://github. com and Gitlab, http://gitlab. com), there is also strong encouragement to make code re-usable, shareable, and citable, via DOI or other persistent link systems. For example, GitHub projects can be connected with Zenodo for indexing, archiving, and making them easier to cite alongside the principles of software citation  [25]. This all comes as part of a wider global shift towards more open research practices, and the geochemistry community needs to make sure it is engaging widely with these, as well as connected issues like research integrity and reproducibility, if it is to maintain its relevance in the modern research age.
Data Analysis Quantitative Analysis Data analyses were run using R 3. 4. 1. software with "FactoMineR" and "qmethod" packages. The analytical process is described by Zabala  (23). Data are presented in a matrix with statements in rows and Q-sorts in columns. First the intercorrelation matrix is calculated, it represents the correlation between each Q-sorts. Then, this matrix is reduced, and factors are extracted using Principal Component Analysis (PCA). Adversely to a classic PCA, Q-sorts are the variables and disposed in columns in the matrix. The number of principal components chosen for further analysis are based on the following criteria: the eigenvalue of the component should be greater than one, the total variance explained >40%, more than two Q-sorts should be loaded by factor and the factors should make sense once the analyses is completed.
Qualitative Analyses Recorded interviews were transcribed and then translated into French or directly transcribed into French on WORD Microsoft Office 365 software version 2016. Where a recording device was not used, interviews were recorded in writing. The different factors were analyzed and interpreted using the ABC model of attitudes for sociological sciences  (25). This model helps to study attitudes by describing them through three components: Affective, Behavioral, and Cognitive. Attitudes toward antibiotics were decomposed into three components: an affective component (feelings of breeders and drug vendors toward antibiotics, alternatives and relationships between the two categories of population), a behavioral component related to action (the AMU, advice and alternative usages) and a cognitive component (belief and knowledge about AMU, risks and alternatives).
Presentation of the P-Set Thirty-one interviews were conducted in the area of Imerintsiatosika. Two farmers did not meet the inclusion criteria (no use of antibiotics and over 2,000 poultry). During data analysis, three Q-sorts (so three sets of answers from respondents) were found to be confounding and were removed from the analysis. Out of the 26 respondents, the majority were men (17/26) and younger than 40 years of age (18/26). Participants were mainly educated (19/26 reached at least high school). Most breeders owned their farm (21/26), had at least one pig (21/26) and worked according to a semi-intensive model  (16/26). The number of farms located in rural areas was quite similar to the number in urban areas (46 and 54%, respectively). Finally, the average number of years of experience was 10.
Material and methods We applied the same method as described in Tennant and Lomax  [9]  for analyzing paleontology journals. The full dataset from Pourret et al. [6]  was updated to include APC and publication rates for OA articles in 2018 and 2019. We only analyze two years because of the dynamic nature of the data (especially for APC). Information for these APCs, rates for OA publication were sourced from the Web of Science, Scopus and journal web pages in January 2020. Key data are available in Table  1  and the full data are available as electronic Supplementary Information and from this online repository (https://zenodo. org/record/3659528). Table  1  List of journals in geochemistry detailing their impact factor 2018, their APC (in US$) and their status (either fully OA, hybrid or none).
Results and discussion The number of 'gold' OA articles started to increase in the early 2000's and continued rapidly over the last 10 years (Figure  1a ). A shift occurred 5 years ago with a decrease of OA articles from hybrid journals (i. e. mixed, OA and non OA articles) towards an apparent preference for articles in fully OA journals (i. e. journals that publish 100% OA articles). Indeed, the first fully OA journal in Geochemistry was launched in 2000 (Geochemical Transactions  [9] ) and since then this number has regularly increased each year (Figure  1b ), with the last title launched in Autumn 2019 (Results in Geochemistry). Among the 56 journals considered here,  16   https://ministeresuprecherche. github. io/bso/) and 45% of the Danish annual research production being uploaded into university repositories (https://www.
In the context of the United Nations Sustainable Development Goals, alternative routes that are more equitable are required for the communication of scholarship with wider society. Such a function has recently been highlighted through the impact of the 2020 coronavirus pandemic, with a clear role for the wider geoscience community to play  [16]. This could include exploring alternative publishing models whereby research outputs are more accessible to benefit society rather than as commercial commodities  [17]. Regardless of any potential waiver or discount systems being in place (including through negotiations between universities and publishers), the total APC paid by the geochemistry community is estimated at US$   2 ). We note that the dataset is not normally distributed (Table  2 ). Publishing in a fully OA journal is substantially less-expensive than publishing in a hybrid journal, on average around half the cost for an equivalent output.
This APC-dominated philosophy has created a complex system and hierarchy of financial privilege around OA publishing  [5]. In this situation, those researchers who can afford to publish in OA journals, and in particular those which have a high JIF and charge high APCs are given an advantage over those who do not benefit from such financial security and are restricted in choice imposed by their inability to afford APCs. A third group can also be considered who are more ambivalent about making their work OA, stating that it is not their "job" (https://ministeresuprecherche. github. io/bso/). It would be of interest to also evaluate the demographic context of constraints on APCs on publication choice and the potential impact this can have on the visibility and re-use of geochemical research. Given that we know OA publishing tends to lead to increased 'impact' for researchers  [22] , the inherent bias of current APC-based OA publishing perpetuates this through the 'Matthew Effect' (i.
In  [8] , the authors provide a review of modeling chain techniques for traffic pollutant emissions, atmospheric dispersion, and effects on water quality. The modeling chain studied here consists of the dynamic traffic assignment model LADTA  [11, 12] , an emissions model Pollemission  [13]  based on COPERT-IV emissions database  [14] , and a Gaussian AQM, Sirane  [15]. The computation of a pollutant concentration field over the agglomeration for any given time requires the solution of each model in the chain, which proves costly for long time periods. This brings us back to MOR techniques. However in this case, we have a chain ofmultiple models to reduce, which leads us to questions on the implementation of MOR techniques : could a single reduction over the full chain be feasible, or will satisfactory results require a chain of meta-models ? How can we treat the large parameter dimension of the chain ? We resort to projection-based MOR techniques based on Reduced Basis (RB)  [16]  to construct cheap and accurate meta-models.
Traffic Emissions Modeling Traffic emissions modeling is done using the dynamic traffic assignment model LADTA. A meta-model was constructed  [17]  to represent the traffic flow and speed simulations over a road network of 19, 628 oriented links, where nearly 45, 000 traffic flow observations are available each day. Emissions of NOx and PM are computed using Pollemission code  [18]  based on the COPERT-IV emissions database  [19, 20]. A detailed description of this section of the modeling chain and its input parameters can be found in  [17]. The varying input parameters consist of 23 traffic parameters and 6 emissions parameters. These parameters are time-dependent or considered sources of uncertainty. They include temporal traffic demand, computed using traffic observations, the capacity and speed limits of traffic network links, multiplicative coefficients on origin-destination matrices representing spatial trends of traffic, traffic direction (morning versus evening), engine size, bype, and emission standards of the vehicle fleet, and ratio of heavy-duty vehicles to personal cars.
Acknowledgments This research is supported by the  French National Research Agency (ANR , the  Agence Nationale de la Recherche) , project  ANR-13-MONU-0001 ,  ESTIMAIR. The  City of Clermont-Ferrand  provided traffic flow measurement data. SMTC ( Syndicat Mixte des Transports en Commun de l'agglomération clermontoise ) provided the traffic network geometry for the agglomeration of  Clermont-Ferrand  and the static O-D matrix representing spatial traffic demand in the traffic assignment model. The SME NUMTECH provided data necessary for the model SIRANE, including geometrical features of the traffic network, meteorological data, emissions data, and background concentrations. We particularly thank  David Poulet  for his contributions to the data treatment. The SIRANE model was developed by " Laboratoire de Mécanique des Fluides et d'Acoustique" ,  UMR CNRS 5509 ,  University of Lyon, Ecole Centrale de Lyon, INSA Lyon, Université Claude Bernard Lyon I.
COVID-2019 has been recognized as a global threat, and several studies are being conducted in order to contribute to the fight and prevention of this pandemic. This work presents a scholarly production dataset focused on COVID-19, providing an overview of scientific research activities, making it possible to identify countries, scientists and research groups most active in this task force to combat the coronavirus disease. The dataset is composed of 40,212 records of articles' metadata collected from Scopus, PubMed, arXiv and bioRxiv databases from January 2019 to July 2020. Those data were extracted by using the techniques of Python Web Scraping and preprocessed with Pandas Data Wrangling. In addition, the pipeline to preprocess and generate the dataset are versioned with the Data Version Control tool (DVC) and are thus easily reproducible and auditable.