dataesr/scientific-paragraphs-categorization huggingface dataset:
In communities of sessile organisms such as trees, IV has been commonly structured in space using individual random effects, which vary among conspecific individuals but stay constant through the lifetime of individuals  (Clark et al. , 2007; Vieilledent et al. , 2010). We here argue that while this approach can reveal the spatial structure of IV through inference, the use of the resulting estimated standard deviation term to introduce individual variation in simulations of community dynamics is not sufficient to produce a spatially structured IV, as we showed is observed in natural communities. Similarly, individual attributes can change over time. Because individuals within a species can be measured at different points in time, as it is often the case when assembling functional trait databases, for example  (Albert et al.
DATA AVA I L A B I L I T Y S TAT E M E N T The appendices and all the code used for this study are available in a GitHub repository (https://github. com/camil legir ardte rcieu x/co-exIV) and have been permanently archived on Zenodo (https://doi. org/10. 5281/zenodo. 5504013). No new data were used in this study. For access to forest plot inventory data and Eucalyptus plantation data used in this study, refer to the data used in Le  Bec et al. (2015) ,  Hérault and Piponiot (2018) ,.
6 μL of HR1 at 250 μM were added to another side of the well. The fusion reaction was initiated by shaking the plate in order to mix the three different solutions. Lipid mixing was measured by following fluorescence dequenching of the NBD probes from the donor liposomes resulting from their dilution into the acceptor liposomes. The NBD fluorescence was monitored at 1-min intervals for 90 min (excitation at 460 nm, emission at 535 nm, cutoff at 530 nm) by the SpectraMax M5 microplate reader (Molecular Devices) equilibrated to 37 °C. After 90 min, 10 μL of 2. 5% (w/v) DDM was added to completely dissolve the liposomes and thus measure the NBD fluorescence at infinite dilution, Fmax(NBD). The data were then normalized using the following equation that gives the percentage of NBD fluorescence increase at time t, %F(NBD, t): %F(NBD, t) = [F(NBD, t) -Fmin(NBD)] / [Fmax(NBD) -Fmin(NBD)] (1) where Fmin(NBD) is the lowest NBD fluorescence value from all time points.
However, many of these assumptions have been inferred and extrapolated from the impacts of other similar manmade infrastructures built in similar environments and thus, little empirical evidence is usually provided for the specific context of PV installations  [46]. To our knowledge, despite the urgent and continuous need to disentangle the effects of PV installations on biodiversity, notably due to the current expansion of the PV market, no systematic map has been attempted so far. Moreover, for most reviews, authors rarely indicated their literature search strategies, nor did they provide an easily accessible database of the literature they collated, nor did they attempt to assess the internal validity of the studies they discussed. Hence, we propose this systematic map-which includes a critical appraisal of study validity-which synthesised the available evidence regarding the effects of PV and solar thermal (hereby both simply referred as PV) installations, whatever their scales (i.
Deviations from the protocol First, during citation retrieval, it appeared that the access to the OFATE (French and German Agency for Ecological Transition) database was restricted and therefore, no search could be performed on this specialised website. Screening eligibility criteria were slightly refined in order to better match our mapping objectives. First, we more accurately defined our exclusion population criteria and thus, considered urban and agricultural habitats and ecosystems (i. e. areas in terms of land use/land cover) as being neither natural nor semi-natural. In addition, we considered the different interventions that can be carried out at USSE facilities such as mowing, grazing or rehabilitation (with various types of seed mixes) to be relevant to our research objectives. On the contrary, the exposure to PV-powered devices such as global positioning tracking systems for animals, water pumps or lamps was considered to be out of scope of our systematic map.
g. mowing, grazing, rehabilitation) or different contexts being compared (e. g. location, surrounding ecosystems). Information on the availability and location of data within articles were extracted as well. Furthermore, we followed the more recent Köppen-Geiger climate classification provided by Rubel et al. [50]  instead of the classification by Peel et al. [51]  proposed in the protocol. Finally, to improve metadata extraction and critical appraisal accuracy and quality, we tested reviewers' agreement on a subset of 20% of accepted articles instead of the 5% proposed in the protocol. Study internal validity criteria were also slightly rephrased to further improve clarity and homogeneity with the CEE Critical Appraisal Tool (CEECAT) (see section 'Study validity assessment'). As stated in the protocol  [49] , critical appraisal was performed on studies which corresponded to one experimental design.
Search for literature Languages Searches were performed using exclusively English terms-which, based on the specificities of online publication database search engines, enable literature written in any other language to be retrieved. Only studies published in English or French were retained in this systematic map. We acknowledge that only including articles in these two languages constitutes a potential bias to our systematic map. Unfortunately, this could not be avoided based on the linguistic competences of the review team. The list of search terms is presented in the section below.
Search terms and string A scoping exercise was carried out on the Web of Science Core Collection (WOSCC) database in order to build the search string. In order to reach the best search comprehensiveness and accuracy, several combinations of search terms describing populations and exposures were trialled iteratively (see protocol additional files  [49] ). Citations were finally retrieved using the following search stringwhich followed Web of Science format and was performed on topic 'TS': TS = ((photovoltaic$ OR "solar panel$" OR "solar array$" OR "solar development$" OR "solar power" OR "solar park$" OR "solar installation$" OR "solar facilit*" OR "solar plant$" OR "utility-scale solar energ*" OR "utility scale solar energ*" OR biosolar OR "float* solar" OR floatovoltaic$) AND (biodiversity OR ecolog* OR ecosystem$ OR wildlife OR "natural habitat$" OR species OR flora OR vegetation$ OR animal$ OR fauna OR vertebrate$ OR mammal$ OR bird$ OR reptile$ OR amphibian$ OR invertebrate$ OR arthropod$ OR insect$ OR arachnid$ OR crustacean$ OR mollus* OR microbi* OR bacteri* OR microorganism$ OR fung*)).
Online publication databases Using the access rights provided by the MNHN and the CNRS, we conducted the search on four multidisciplinary publication databases: Web of Science Core Collection (WOSCC), Biological Abstracts (BA), Zoological Records (ZR), all from Clarivate Analytics, as well as Scopus from Elsevier. All databases were selected for their relevance in the matter of ecological studies and for easy search reproducibility and accessibility. The WOSCC search included the following citation indices: Science Citation Index Expanded (SCI-EXPANDED, 1956-present), Social Sciences Citation Index (SSCI, 1975-present), Arts & Humanities Citation Index (A&HCI, 1975-present), Conference Proceedings Citation Index-Science (CPCI-S, 1990-present), Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH, 1990-present), Book Citation Index-Science (BKCI-S, 2005-present), Book Citation Index-Social Sciences & Humanities (BKCI-SSH, 2005-present), Emerging Sources Citation Index (ESCI, 2017-present), Current Chemical Reactions (CCR-EXPANDED, 1985-present) and Index Chemicus (IC, 1993-present).
As for BA, ZR and Scopus, we had access to all indexed databases (respectively 1969-present, 1864-present and 1788-present). Searches for WOSCC, BA and ZR were performed on 17 June 2022. We then adapted the abovementioned WOS search string to match the Scopus format for literature search (see Additional file 2). The search on the Scopus database was then carried out on 20 June 2022.
Internet searches An additional search was carried out using Google Scholar on 28 June 2022 using the software Publish or Perish (v 8. 2. 3944, downloaded on 07 June 2022)  [53]. The search string was simplified and divided into four to fit the search capabilities of this engine-limited Boolean operators and a maximum of 256 characters  [54]. Searches were performed on titles exclusively. The first 250 hits of each search string were retained which resulted in 677 references being added to the literature database (see search strings and hits in Additional file 2).
Supplementary searches A call for literature was conducted through a professional network to find non-peer reviewed literature in English and/or French-i. e. technical reports, MSc or PhD theses. The call was initiated on 6 September 2022 and provided 234 additional references to our literature database (see Additional file 2). Finally, 8 relevant citations not retrieved by the literature search were added after being identified by the review team throughout the mapping process (see Additional file 2).
Estimating the comprehensiveness of the search A test list of 26 relevant primary research articles was established in order to assess the comprehensiveness of the literature search. These articles were identified by the review team, with the help of experts or through previous syntheses on PV installations and biodiversity carried out by French operational actors such as the ADEME (French Agency for Ecological Transition)  [42] , the LPO  [43]  as well as the IUCN (International Union for Conservation of Nature)  [40]. Among the 26 articles of the test list, 25 were indexed in WOSCC and 25 in Scopus-both thus provided a 96% indexation level, indicative of a high degree of relevance of these two databases for our literature search. As for BA, it had 18 indexed articles belonging to the test list (72%).
On these three databases, one article from Bousselot et al. [52]  was consistently not indexed. We thus checked its presence on ZR but it was not indexed in this database either. Among the 26 articles of the test list, our search string retrieved 100% of the articles indexed in WOSCC (25 articles), in Scopus (25 articles) as well as in BA (18 articles). Details on search hits from each selected database can be found in Additional file 2.
g. mowing, grazing, rehabilitation) as well as studies comparing different contexts such as different climatic conditions or different ecosystems surrounding USSE facilities were included. We considered all possible contents being primary research, reviews, meta-analyses or modelling studies but discarded LCA modelling studies. Reviews and meta-analyses were separated from the main literature database and their metadata coded in another datasheet. Conference objects (e. g. meeting abstracts, slides, posters) were excluded because of their relatively low content in useful data and information. The list of excluded citations at the full-text stage alongside reasons for exclusion is provided in Additional file 2.
2 ), availability and location of data. Then, AL independently extracted the metadata for all remaining articles. At the end of the metadata extraction phase, Fig. 2  The different types of comparators used during metadata extraction. PV Photovoltaic and solar thermal; USSE Utility-Scale Solar Energy. Created using images provided by Freepik YR cross-checked 5 articles extracted by AL (5. 2%)-corresponding to 35 observations. Special care was taken to ensure no reviewer ever had to extract metadata from articles they co-authored.
Data mapping method An open-access database of all observation data with associated study-level overall risk of bias was produced alongside this systematic map report (see Additional file 4). A geographic map, tables and bar charts were used to represent the distribution of key variables extracted during metadata extraction-i. e. source, document type and content, year of publication, location, internal validity assessment result, population, exposure, comparator, outcome. In addition, heat maps showing the distribution of observations by population, comparator and outcome were produced in order to identify key knowledge clusters and gaps. In the case of this map, an arbitrary threshold of 100 observations was chosen to identify the main knowledge clusters, deemed to be sufficiently well represented for a future systematic review to be considered.
Results of the search for literature Searches on online publication databases yielded 3,978 citations for Web of Science Core Collection, 1,012 for Biological Abstracts, 102 for Zoological Records and 6,130 for Scopus. Additional searches on Google scholar and specialised websites provided respectively 677 and 20 citations. Finally, the call for grey literature provided an addition of 234 references and the review team also provided 8 relevant citations which were not otherwise retrieved (see Additional file 2). Out of the overall 11,980 retrieved citations, 8,121 unique references were kept after duplicate removal (Fig. 3 ). Title screening resulted in 1,076 citations being accepted, for which we were able to collect 974 full-texts. The remaining 102 missing fulltexts (9. 5%) were either unavailable to us or could simply not be found.
Sources and document types of included articles About three quarters of accepted articles were retrieved by the main searches on online publication databases (117 articles, 74. 1%) (Fig. 4 ). The searches on Google scholar yielded 10 additional articles (6. 3%). Then, the call for grey literature provided a substantial number of supplementary references with 18 articles (11. 4%). Searches on specialised websites resulted in 9 articles (5. 7%) being accepted. Finally, four additional articles (2. 5%), which were found by the review team but had not been otherwise retrieved, were also included. Accepted articles consisted mainly of journal articles (71. 5%) and were more often written in English (92. 4%). In addition, 25 technical reports (15. 8%), 9 conference proceedings (5. 7%), five MSc theses (3.
Geographical distribution of included observations After carrying out critical appraisal, the 137 non-redundant studies were further split into 434 observationsi. e. corresponding to one population and one outcome. The United States were the primary research location with 100 observations (23. 0%), closely followed by the United Kingdom with 91 observations (21. 0%) and then France with 52 observations (12. 0%) (Fig. 7  and Additional file 5). When only considering observations from online publication databases and Google scholar, the Fig. 3  ROSES flow diagram reporting the screening process of the articles, studies and observations included in the systematic map  [67]  Czech Republic becomes the third most studied location with 23 observations (6. 4% of the 359 observations in this subset) and France, joint ninth with Japan and Israel, with just 11 observations (3.
Limitations of the systematic map method The first limitation of this systematic map concerns the search for literature. The term 'renewable energy' was not used in the search string as it was deemed too broad for the scope and duration of the mapping process. While this term could have made our search too noisy, some relevant citations may still have been missed. Indeed, some citations identified and added by the review team only used the key term 'renewable energy' in their titles, abstracts or keywords (topic 'TS') and were therefore not retrieved by our search on online publication databases. However, as we extensively tested our search string prior to retrieving any citation, we believe that our corpus should be comprehensive and missed citations limited. Likewise, the search term 'solar thermal' was also excluded while building our search string because it resulted in too many citations referring to Concentrated Solar Power, an exposure excluded from this map.
Acknowledgements The authors particularly thank  Hugo Mell  and  Jade Ka  for their help in retrieving full-texts. We also thank  François Robelet  for his precious help when organizing the inter-library loan for full-texts. Finally, we particularly thank  Véronique de Crespin de Billy ,  Sébastien Filoche ,  Jérémy Froidevaux ,  Nicolas Hette-Tronquart ,  Geoffroy Marx ,  Bertrand Schatz  and  Sylvie Vanpeene  for helping us gather precious grey literature.
IEA (International Energy Agency): https:// www. iea. org/ -IRENA (International Renewable Energy Agency): https:// www. irena. org/ -United States Office of Energy Efficiency and Renewable Energy: https:// www. energy. gov/ eere/ officeenergy-effic iency-renew able-energy -ADEME (French Agency for Ecological Transition): https:// www. ademe. fr/ Searches were carried out on 20 and 21 October 2022 and added 20 additional references to our literature database (see Additional file 2). We could not search for any citations on the OFATE (French and German Agency for Ecological Transition) website due to restricted access rights.
Human movement analysis is a key area of research in robotics, biomechanics, and data science. It encompasses tracking, posture estimation, and movement synthesis. While numerous methodologies have evolved over time, a systematic and quantitative evaluation of these approaches using verifiable ground truth data of three-dimensional human movement is still required to define the current state of the art. This paper presents seven datasets recorded using inertial-based motion capture. The datasets contain professional gestures carried out by industrial operators and skilled craftsmen performed in real conditions in-situ. The datasets were created with the intention of being used for research in human motion modeling, analysis, and generation. The protocols for data collection are described in detail, and a preliminary analysis of the collected data is provided as a benchmark.
In such context, numerous motion capture initiatives have been undertaken in various fields. Most of them have been made publicly available for visualization and analysis, and they include data corresponding to different activities of human everyday life covering from ample body motions to very fine facial expressions. These datasets can be categorized based on the technologies used (markerbased or marker-less motion capture, etc. ), on the activities recorded (everyday activity, sports, etc. ), or on the number of users (single user vs. multiuser interaction). For example, HumanEva and MoVi  [1] ,  [2]  are existing datasets that  VOLUME 11, 2023  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4. 0 License. For more information, see https://creativecommons.
org/licenses/by-nc-nd/4. 0/ contain video and marker-based motion capture (MoCap) data of a single person performing ordinary activities (like walking and jogging) and sports motions. General body movements were also recorded with a monocular camera in the HMDB51  [3]  dataset, including user-object interaction, human-to-human interaction, and facial expressions. A multimodal human action database MHAD  [4]  has been published, including limb actions recorded with a camera, accelerometers, and a microphone. Another more recent initiative has been done in the CMU dataset  [5] , also including the multimodal signal from food preparation activities. For motions in multiperson interactions and scenarios, the UMPM benchmark was presented  [6]. Also, the KIT dataset recorded human-to-human interaction activities while manipulating various objects  [7].
In all the aforementioned studies, everyday activity has monopolized the interest of researchers performing motion capture. However, in the last decade, it has become more and more interesting to use motion capture and to apply data analysis methods to scenarios inspired by a professional context where human operators perform their tasks. The table below presents recent works of industry-oriented human motion data. Several examples can be found in the construction industry since it is one of the most affected by intense physical activity. To detect excessive load-carrying tasks, Lee et al. [8]  have focused on creating a dataset with a non-invasive single IMU sensor. The recorded data has served to automatically predict load-carrying weights and postures using deep learning algorithms. Fourteen subjects were recorded performing six different carrying modes.
The dataset's analysis consists of modeling, classification, and predicting load-carrying weights. Another interesting dataset was recorded in the framework of the AnDy EU project  [9] , where various sensors were used, such as a full-body IMU suit including a glove for finger motion, a marker-based motion capture system, a finger pressure sensor, and 2 video cameras. The subjects performed industry-oriented activities inspired by car manufacturing. The data was annotated and labeled and is intended for use by researchers developing algorithms for classifying, predicting, or evaluating human movement in industrial settings. The evaluation focuses mostly on label reliability, not movement analysis itself. The VTT-Conlot dataset includes motion data inspired by the construction industry recorded with 3 IMUs, with 13 subjects  [10].
The principal goal of this dataset is to be used for activity recognition and classification. Its evaluation refers to sensor location, modalities used, and features extracted. However, contrary to previous examples cited, the VTT-ConIot validated and compared its data also with real unannotated data belonging to real workers in a real construction site (the real data is not included in the VTT-Conlot dataset). The IKEA ASM dataset is a multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose  [11]. One of the particularities of this one is that it includes unusual human poses performed while assembling furniture, but it does not include any IMU-captured data and aims mostly at solving computer vision challenges. The WGD dataset provides data recorded with a marker-based system of subjects performing assembly line working activities  [12].
A kinematic evaluation of the data has been performed, showing that the dataset can be used for human ergonomics evaluations. Finally, the Workflow Recognition (WR) dataset comprises multi-camera video sequences recorded from the production line of an automobile manufacturer. The WR dataset was created mainly to test activity and workflow recognition algorithms  [13]. All the aforementioned works went beyond recording everyday activities and focused on professional tasks/gestures/postures. However, there is still a need for MoCap data that include a greater diversity of movements, particularly professional gestures captured in real-world scenarios. Most of the datasets available were recorded inside a laboratory, causing approximate measures since they may lack authenticity and are not real workplace scenarios.
Thus, this paper presents datasets created to capture and study operators' and artisans' gestures in their professional settings and real environment, performed under real conditions. The recording procedures and processing methods are detailed in this paper. Additionally, it is provided a first analysis of the seven datasets using an analytical model called the Gesture Operational Model (GOM), which was proposed in a previous work  [14]. In this analysis are created interpretable motion representations based on GOM that can be used to artificially generate human movements and explain the inter-collaboration of joints during the performance of the modeled movements. The results comprise the forecasting performance measures on every dataset and a dexterity analysis of professional tasks. The dexterity analysis applies GOM's mathematical representations to describe the performance of professional gestures.