dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). However, LENA word count scaling is highly recommended for other languages if one wishes to use LENA to measure not only relative but also absolute word counts in an accurate manner. From a technical point of view, the study demonstrates the applicability of supervised training of a neural network-based syllabifier. When multiple different training languages are used at the same time, such a syllabifier is also capable of reaching consistent behavior in languages not included in the training data. Furthermore, the BLSTM syllabifier outperformed two previously used syllabification methods, including the WN algorithm used in the WCE system of  Ziaei et al. (2016)  , which had outperformed multiple alternative syllabifiers in the experiments of Ziaei et al. In addition, training data augmentation using a variety of realistic additive noise types and channel variability (based on random FIR-filters) was found to consistently improve syllabifier performance in the WCE task where signal conditions are extremely difficult compared to any typical speech processing problem.
In the current corpora, all speech that the annotators could transcribe based on repeated listening had been transcribed, while the unclear vocalizations are simply marked as "cannot transcribe ". In practice, there is a continuum from clear nearfield speech to hardly audible noisy content that is only partially comprehensible. Since the current datasets do not provide any information on the clarity of the input for the transcribed words, all transcribed tokens from this continuum are treated as equally relevant targets for the WCE. Another potential source of uncertainty comes from the potential differences in how faithfully spoken language maps into orthographic transcripts, and especially how well whitespaces in the orthographic transcripts can be used to define word boundaries in the running speech in different languages compared.
Acknowledgments This research was funded as a part of Analyzing Child Language Experiences around the World (ACLEW) collaborative project funded by the  Trans-Atlantic Platform for Social Sciences and Humanities "Digging into Data  " challenge, including a local  Academy of Finland  grant (  312105  ) to OR,  ANR-16-DATA-0004  ACLEW to AC,  NEH HJ-253479-17  to EB,  HJ-253479  to CR, and funding from the  Social Sciences and Humanities Research Council of Canada  to MS (  869-2016-0003  ). MC was funded by an  NWO   Veni Innovational Research grant  (  275-89-033  ). In addition, OR was funded by an  Academy of Finland  grant no. 314602  , MS by a  Social Sciences and Humanities Research Council of Canada   Insight Grant  (  435-2015-0628  ), and AC by  Agence Nationale de la Recherche  (  ANR-14-CE30-0003   MechELex  ,  ANR-17-EURE-0017  ) and the  J.
cefas. co. uk/nns/tools/). For each taxon screened, whenever applicable the scientific name used in the original study was updated to the most recent taxonomy after FISHBASE (Froese and Pauly 2018), followed by 'cross-checking' for the existence of at least one peer-reviewed published study that adopted the updated scientific name. This criterion also applied (in principle) to the common name, except for those taxa for which an 'official' name is not (yet) available, in which case the most frequently used common name in English, or the vernacular name as per the original study, was employed. The taxonomic order and family were also retrieved for each taxon screened. FISK applications were distinguished into those that provided calibration of the outcome scores and those that did not. In the former case, a distinction was made whether the a priori classification of the taxa into either 'non-invasive' or 'invasive' (a requirement for calibration: see below) was according to both FISH-BASE and the Global Invasive Species Database (GISD: www.
Scoring and certainty The shape of the global distribution of FISK scores was tested in R x64 v3. 4. 3 (R Development Core Team 2015) using package moments v0. 14  (Komsta and Novomestky 2015) , with normality, skeweness and kurtosis evaluated by the Jarque-Bera (JB), D'Agostino and Anscombe tests, respectively. Differences between mean scores for the taxa classified a priori into non-invasive and invasive (based on the original, updated or new a priori classification, as applicable: see Outcomes), and according to FISK version and RA area, were tested by Permutational (Univariate) Analysis of Variance (PERANOVA). This was based on a partial-hierarchical design (cf. Vilizzi 2005)  with factors Category (non-invasive, invasive),  Version (v1, v2)  and RA area (see Table  1 , but excluding Great Lakes Basin, Northeast of ParaBasin, Puerto Rico, Rhine Basin and River Oder Estuary, due to low samples sizes) nested within Version, and with all factors fixed.
Differences between applicationspecific AUCs were tested for all possible pair-wise combinations of RA areas, but separately under FISK v1 and v2 and after excluding those AUCs equal to 1 or less than 0. 5  (Zhang and Pepe 2005). ROC analyses were carried out in R with package pROC  (Robin et al. 2011 ) using the default 2000 bootstrap replicates for computation of the AUC confidence intervals and the DeLong test for a posteriori pair-wise comparisons with Bonferroni-corrected significance values. Log-linear analysis  (Quinn and Keough 2002)  was used to determine the effects of RA area (except for Great Lakes Basin, Northeast of ParaBasin, Puerto Rico, Rhine Basin and River Oder Estuary, because of the low samples sizes: Table  1 ), a priori classification (non-invasive, invasive: original or updated a priori classification, as applicable), and risk level (low, medium, high: see ''Toolkit description'') on the number of taxa screened, and separately for FISK v1 and v2.
As a result, 'real-world' FISK scores are necessarily expected to be confined within a more restricted range of 'ecologically-meaningful' values as opposed to the full, 'computationallypossible' set of all values. Finally, the observed rightskewness (i. e. towards higher values) in the overall distribution of FISK scores reviewed in the present study would indicate a propensity to assess proportionally more taxa likely to be invasive in the RA area under study, as in the case of those taxa included in local 'black/grey' lists and/or global databases of invasive organisms (e. g. Essl et al. 2011; Matthews et al. 2017). In the present study, reporting of the statisticallysignificant higher value in overall mean score under FISK v1 relative to FISK v2 per se was mainly driven by 'illustrative' rather than statistical reasons.
Acknowledgements Support for participation was provided to: GHC by  Cefas  and the  UK Department of Environment , Food & Rural Affairs; BA by the  Belarus Republican Foundation for Fundamental Research ; A ´F by a  Bolyai Janos Fellowship of the Hungarian Academy of Sciences ; SMM by the  NRF Professional Development Programme  (Grant No. 1010140 ); RM by the  Commission for Knowledge and Use of Biodiversity (CONABIO) , Mexico; MP by grants from the  Croatian Science Foundation  ( IP-06-2016 ) and the  University of Zagreb  ( 1-28-121 ); FR by  FRISK  Project ( FCT Ref. PTDC/ AAG-MAA/0350/2014 ) and by the strategic plan of  MARE (Marine and Environmental Sciences Centre :  UID/MAR/04292/ 2013 ); PS by grants from the  Ministry of Education, Science and Technological Development of Serbia  ( OI173025 ) and the  Croatian Science Foundation  ( IP-06-2016 ); OLFW by the  National Research Foundation-South African Research Chairs Initiative of the Department of Science and Technology  (Grant No.
A sparse network consisted of a maximum of 200 nodes, a medium network ranged between 201 to 400 nodes, and a dense network consisted of more than 400 nodes. Performance evaluation metrics included clock synchronization, channel utilization, message loss rate, end-to-end delay, and throughput, which are commonly used in the state-of-the-art to evaluate MAC protocols in VANETs  [22, 33, 34, 48]. Analytical results, obtained on MATLAB R2018a, were used to validate the simulation results for the proposed PDMAC protocol. Each result presented was averaged over 20 replicated simulation runs by keeping all parameters fixed and changing the random seed values.
For this reason, to perform tests with real UAVs, one needs access to expensive hardware and field tests that are costly, time-consuming, and require trained and skilled people to pilot and maintain the UAVs. Moreover, in the field, it may also be hard to reproduce the same scenario several times  [10]. To overcome these limitations, simulation frameworks have been developed to allow transferring real-world scenarios into executable models (i. e. simulating UAV activities in a digital environment). An agent is defined as an autonomous software entity that is situated in some environment and where it is capable of actions and coordination with other agents to achieve specific goals  [11]. For these reasons, the resulting Multi-Agent System (MAS) technology have been established as a suitable platform for implementing autonomous behavior and decision-making in computer systems  [12, 13].
Defining The Review Protocol As shown in Fig. 1 , defining the review protocol is done right after having set the RQs. The protocol we used for this article involves the following steps. First Section 2. 2. 1 chooses the databases used as sources of information and defines the stop criterion. Section 2. 2. 2 defines the exclusion/inclusion criteria used by the reviewers to exclude/include articles chosen from the databases before the stop criterion was triggered. Section 2. 2. 3 presents the quality criteria used by the reviewers to assess the quality of the primary studies. Finally, Section 2. 2. 4 explains the policies used to mitigate subjective biases and resolve conflicts.
Database Selection This process is composed of the following couple of steps: a) IEEE Xplore, ACM Digital Library, and Google Scholar are selected as the three databases constituting the source of information. The selection of the first two databases is obvious in computer science. Google Scholar is selected because it provides a large list of documents that are not indexed into the two previous databases, e. g. papers from conference proceedings, Ph. D. and Master theses. Despite not being peer-reviewed, these articles obtained from Google scholar might be important given that the interest in the studied topic is rapidly increasing in the recent years. b) The databases are queried with a set of keywords. These keywords are devised based on the authors knowledge of the UAV and MAS domains.
When queried with these keywords, each database responded with a set of articles that are considered by the reviewing process. The numbers of articles to be produced by the queries is relatively large for IEEE Xplore, ACM DL, and Google Scholar databases. However, only a few of these articles were relevant to the research questions raised in the previous section. For this reason, as in  [20] , the following stop criterion was applied: "Stop the collecting of articles after a sequence of 10 titles, completely incoherent with the query, appeared in the list. " Determining whether an article is coherent is left to the reviewers' subjective view when they deemed that there was no adherence between the query performed on the database and the title/abstract of the article appearing in the result.
Performing The Review This section gives an account of how the SLR has been conducted and discusses the results of the exclusion/inclusion step. Two keyword sets were applied to the three databases (IEEE Xplore, ACM DL, and Google Scholar). The first keyword set is {UAV, agent, simulation}, whereas the second keyword set is {UAV, agent-based simulation, drones, civil, multi-agent systems}. A stop criterion of 10 articles was applied to the results of each keyword set and database. After the stop criterion was applied, the total number of articles retained was 316. The next step is to apply the coarse-grained exclusion/inclusion step. Note that since this step screens papers based on their titles and abstracts, some exclusion criteria might be more helpful than others (e. g. ExC1 and ExC2).
Demographic Data (RQ1) To understand the evolution of UAV simulations in MAS in the last decade funding projects. This fact may explain the high number of publications for this country compared to its R&D investment, illustrated in Fig. 10. In the mentioned figure, it is interesting to note that, even if civilian UAV-related regulations in USA are less restrictive than in Europe, the number of papers over Europe (32 papers) is three times higher than the number of papers in North America (9 papers). We explain this by the fact that the EU research policy enforces funding on breaking technologies, such as UAVs. Additionally, with a relatively lower R&D investment rate of EU countries (Fig. 10 ), researchers from these countries have fewer opportunities for funding UAV deployment on real fields.
Environment Dynamicity (RQ3. 3) Immersing agents in dynamic physical, virtual or mixed environments is still a challenge for MAS researchers. As has been established in  [73] , an essential part of such systems is the MAS environment, in order to provide the services allowing agents to interact with it. However, defining what is the interface between the agents and their environment is not obvious. A key aspect is to both respect their autonomy and ensure that the environment rules are enforced. Weyns et al. [73]  define agent environment as the software layer between the external world and the agents. Dynamic agent environments include endogenous processes that enable the environment's state to evolve dynamically outside the control of the agent. In a static agent environment, such process is not included.
Evaluation and simulation scenarios (RQ7) The purpose of this research question is related to the evaluation of the proposed models: whether it relies on a dataset, or on a generated synthetic dataset, or no dataset. Only a few of the reviewed papers (5%) have a reference to a dataset for setting up the UAV simulations; 17% of the papers have generated a specific dataset for evaluation; While, the majority have no dataset. In papers with no datasets, simulation scenarios are defined as ad hoc by the authors. This relatively low number of papers with datasets may be explained by the difficulties for building such sets, e. g. it is hard to gather realistic data and initialize a UAV model from it. This issue is related to the research direction RD7 detailed below. Having well-established testbeds with datasets helps to unify the testing process, and allows for systematic comparisons between the solutions proposed by researchers.
However, the comparison lacked some frameworks that were revealed by the SLR provided in this work. The two most used frameworks by the papers in the SLR are AgentFly (7%), Simulink (7%). However, the result indicate that no framework was favored by researchers for civilian UAV applications. The larger part of the of implementations in the reviewed papers is using an ad hoc framework (43%). These simulation frameworks are typically developed by the authors of the reviewed papers from scratch. This fact leads us to consider that the existing frameworks do not cover all the needs mandatory for implementing a UAV simulation software. Moreover, and due to the abundance of frameworks with no clear distinguished features related to UAVs, the authors generally prefer to setup their own configuration and build the simulation framework from scratch even though it is time-consuming.
The growing pressure to innovate and the demand for shorter development cycles require changes in the UAV development methodology. As a result, there is a shift in the demands on testbed systems. This desire for shorter development times stands opposed to the growing complexity re-quired for developing of increasingly automated and autonomous systems  [113]. Enabling early validation of such system designs requires the simulation of components. This requires the development of adapted simulation environment, possibly real-time, composed of a collection of reusable modules combining real and virtual components (also called XiL: X-inthe-Loop, where X meaning alternatively Model/Software/Hardware and Human).
6. Algorithm-based models were used slightly more than the mathematical models by the reviewed papers. 7. To evaluate their contributions, only 5% of papers rely on public datasets and less than 20% use generated datasets. The remaining majority do not use any dataset for evaluation purposes. This underlines the absence of common testbeds and datasets allowing to evaluate and compare these works. 8. In a related finding, the results showed that to conduct their experiments, about 40% of the studied papers implement their own ad hoc simulations. This might be an indication that existing frameworks are not meeting all the needs required for implementing UAV simulations. As a result of this analysis, we outlined the following key challenges and research Regarding the SLR, a future work will be to compare the contributions based on MAS to those using real UAVs.
g. mathematical, algorithm-based, etc. ) This RQ is set up in order to determine if the contributions to civilian UAV applications are formal or semi-formal. It will lead us to a statement and arguments regarding the validation of UAV models. RQ7: Assess the evaluation of the proposed model: Simulation for civilian UAV applications needs scenarios to be set up, and the use of datasets may help to create such simulation in a realistic way. This RQ assesses if the evaluation relies on a dataset, on a generated synthetic dataset, or no dataset. This will highlight the different datasets, and the scenarios (if no dataset is used) from literature. RQ8: Identify the simulation frameworks used to implement the proposed solutions, and the main advantages & disadvantages of each framework especially if it excels in a specific civilian UAV application domain.
RQ6: Identify the main model category used by the proposed work (e. g. mathematical, algorithm-based, etc. ) This RQ is set up in order to determine if the contributions to civilian UAV applications are formal or semi-formal. It will lead us to a statement and arguments regarding the validation of UAV models. RQ7: Assess the evaluation of the proposed model: Simulation for civilian UAV applications needs scenarios to be set up, and the use of datasets may help to create such simulation in a realistic way. This RQ assesses if the evaluation relies on a dataset, on a generated synthetic dataset, or no dataset. This will highlight the different datasets, and the scenarios (if no dataset is used) from literature. RQ8: Identify the simulation frameworks used to implement the proposed solutions, and the main advantages & disadvantages of each framework especially if it excels in a specific civilian UAV application domain.
This paper presents new services developed by the Lille University Library for European and National research project coordinators. This is a specific audience that libraries are not used to target, with a widely recognised institutional status and academic background. Supporting them in their coordination activities is an opportunity to gain a new role for libraries, which starts from the design of research at the submission stage and lasts several years after, during the project lifetime. These services help coordinators to meet their funders' expectations on Open Access and research data management. It is also a way to develop new collaborations with research units and some university services, such as the Grant Office. The Lille University Library has already supported the writing of forty grant proposals since 2017, including about thirty since early 2019.
Introduction The University of Lille is a multidisciplinary university with 3,300 researchers and 66 research units. For several years, the Lille University Library has been developing an ambitious set of support services for researchers. This article presents services 1 dedicated to research projects funded by the European Horizon 2020 2 framework programme (H2020), or by the French Agency for Research 3 (ANR). We introduce the history of our approach before presenting the three main components of this set of services and their implementation. We emphasize that this approach allowed us to develop a deeper relationship with researchers with whom we were not so used to work before. Finally, we will draw some lessons from our experience and expose new challenges we could face in the future.
H2020 Projects, an Open Door for Open Science? The Library of Lille started to work on Open Access to publications in 2012 and on research data management in 2013, but it is not until autumn 2016 that we have developed a strategy to meet the specific needs of research projects coordinators. Due to the H2020 Mandate on Open Access and to the Open Research Data Pilot,  4  European projects seemed to be a good testing ground for supporting researchers with their research data management practices. We contacted our Grant Office in order to identify projects we could work with. However, we quickly discovered that at the beginning of 2017, all the projects coordinated by our University and covered by the first phase of the H2020 Open Data Pilot (2014-2017) had chosen to opt-out, which means they had no obligations regarding data management.
3 Research Data Pilot, we diverted focus to projects subject to the Mandate for Open Access to Publications. In autumn 2017, we had our first meetings with two project coordinators: one ERC Advanced Grant 5 beneficiary and a multibeneficiary H2020 coordinator. They were only partly aware of their Open Access requirements. Both thought that publications had to be published in Open Access on the publishers' platform. Due to the number of their project publications and to the cost of articles processing charges, they published only a selection of their articles in Open Access. Through several interviews with them, we identified their needs and how we could support them to improve their level of compliance with their Open Access obligations. We helped them to spot the publications they had to disseminate in Open Access and we explained them how they could do it into our institutional repository.
When the Door Remains Shut At the end of 2017, the first H2020 project coordinated by a researcher of our university and covered by the Open Research Data Pilot started. We Liber Quarterly Volume 29 2019 expected this project to be the first we could work with on research data management. However, when meeting the coordinator, we discovered that a consulting firm had been included in the project at the grant writing phase and was paid to perform all the tasks to follow-up with the progress of the project, which included monitoring Open Access to publications and the writing of the data management plan. We already knew that such stakeholders were involved at the grant writing phase but we did not expect them to be involved in the post-award phase of the project, which proved to be a naive thought.
We analyzed the ANR grant application template to determine which sections the library could contribute to, such as: the impact section, the work package on project management, the institutional support to the project (IT infrastructure) or the risk analysis related to data management. We managed to convince them to add an Open Science dimension to their project, even though ANR did not have any obligations of this kind at that time. We helped two other coordinators to write their proposal during this call for project. A month later, a researcher who was drafting an ERC Starting Grant 7 proposal asked for our help, after he had heard of the service we were delivering. The door was finally fully opened, which allowed us to implement a new set of services.