dataesr/scientific-paragraphs-categorization huggingface dataset:
2012, Marquèz and Kolosa 2013). The main aim of this study was to quantify the relative contributions of geographical situation (area, distance from the mainland) and time of emergence of the islands, local environmental conditions (elevation, air temperature, rainfall) and dispersal ability of taxa in determining the overall species richness, endemic species richness and species composition of the aquatic entomofauna of the Caribbean islands. We achieved this based on a database of 1027 aquatic insect species across 26 GCUs assembled from the literature. Based on island biogeographic models and theories discussed above, we used our data to test four predictions: (1) Following TIB, we expected island area and distance to island to be the main driver of species richness with a higher richness in larger islands and a lower richness for remote islands.
Species inventories We gathered species inventories on each island based on research works including doctoral theses and scientific papers, Proceedings and United Nations Convention on Biological Diversity (CBD) reports and checklists using "Caribbean aquatic insects" as keywords. Several online databases (e. g. , https://www. odonatacentral. org; https://sites. google. com/site/ distributionaldatabase) or Web portal (e. g. , https:// www. gbif. org; https://www. dutchcaribbeanspecies. org, especially for ARU, BON, CUR, SAB, STE and STM) were also considered to complete our database. Part of the gathered information was verified by a leading scientist (F. Meurgey, pers. com. ). References that were used to gather data are available as Supplementary Information (Appendix S1). Ten orders of aquatic insects were initially considered: Coleoptera, Diptera, Ephemeroptera, Hemiptera, Hymenoptera, Lepidoptera, Megaloptera, Odonata, Plecoptera and Trichoptera.
We linked a unit to its nearest neighbour considering the shortest path between units. We further quantified the spatial autocorrelation among GCUs in terms of species richness using  Moran's I (1950)  statistics  (Cliff and Ord 1973). Positive and significant Moran's I values indicate that nearby GCUs have similar spatial patterns, whereas negative values indicate the opposite. All Moran's I values were further tested using Monte Carlo permutations (999 runs). The above computations were performed for the whole OCTE data and for each OCTE order separately, respectively. We computed both overall OCTE species richness and species richness of each OCTE group separately using the species presence-absence table elaborated from our literature review. In addition, to assess patterns of endemism, we calculated the number of species that were unique to each GCU, i.
Species richness Our database of OCTE Caribbean aquatic entomofauna included 1027 species comprising 192 Odonata, 360 Coleoptera, 405 Trichoptera, and 70 Ephemeroptera. Cuba was the richest GCU (390 species, Fig. 2 ), followed by Hispaniola (290), whereas some islands, such as St Eustatius or Saba, hosted only a few species (6 and 4 species, respectively). Odonata were present in all GCUs, whereas Coleoptera, Trichoptera and Ephemeroptera were absent from 2, 10, and 12 GCUs, respectively (Fig. 3 ). ISAR analyses demonstrated at least one threshold for overall OCTE species richness and for each OCTE insect orders (Fig. 4 , Table  2 ). Continuous one-threshold models fitted overall OCTE and Coleoptera species richness, respectively, whereas discontinuous two-threshold models fitted Ephemeroptera species richness, respectively (Fig.
Limitation of the study Our study provides an analysis of the four best documented insect groups (Appendix S2). Only the information for 26 GCUs could be gathered. From the species accumulation curves computed for the OCTE groups, Odonata and Coleoptera seem rather well documented in our database, whereas the three other groups seem far from reaching a "plateau", suggesting the need for including more sites (Fig. S2 ). However, we accumulated species by GCU, which represented various sampling units. As a result, the plateau may be reach if considering all sampling units. Unfortunately, it is very difficult from checklists, reports or even papers to get the exact locality of samplings, which would have helped to provide more accurate species accumulation curves. We collected information on 6 additional insect orders, using the same documents as for OCTE orders (Diptera, Hemiptera, Hymenoptera, Lepidoptera, Megaloptera, Plecoptera).
Only one Hymenoptera family occurred on one unit (DOM). Lepidoptera identified at the family or genus level appeared on 8 units, and one Megaloptera species on one unit (CUB) was found in our literature review. The data that we gathered for Diptera concerned only species known as vectors of disease, which may bias Frontiers of Biogeography 2022, 14. 3, e54479 © the authors, CC-BY 4. 0 license 14 the distribution among islands. Diptera has long been considered as indicating poor water quality whereas this group has a large range of sensitivity to impairment  (Serra et al. 2017)  and could be given more focus in the area. The two Plecoptera species found in our literature review concerned only Trinidad and Tobago, and should be given more focus as this group is known to be highly sensitive to water quality impairment of (Lenat 1988).
However, this seemed not to affect our results because the slope of SAR for larger islands was steep. In addition, Hispaniola (HIS) comprises two countries (Haiti and Dominican Republic). Haiti has been highly deforested, which could have affected the integrity of rivers whereas the Dominican Republic has better preserved its forested areas. In spite of this, in our data set, Hispaniola remains the second richest island in OCTE. Building a database like ours was time consuming and we advocate aquatic entomologists working in the Caribbean to implement a database of freshwater invertebrates such as those developed elsewhere (e. g. Schmidt-Kloiber et al. 2019). There is an urgent need to collect this information since freshwater ecosystems are among the most endangered in the world  (Dudgeon et al.
Conclusions Finally, the combination of geographical and local filtering variables in our study, with the exception of Coleoptera that was mainly influenced by island area and Ephemeroptera that was mainly influenced by local variables, partially supports the biogeographic conceptual model of  Lomolino (2000). This result is also in line with a recent study by  Vilmy et al. (2021) , who showed a generally more balanced situation between dispersal and niche processes for aquatic macroinvertebrates in comparison to other taxa. However, in our study, the local variables were restricted to climate, but other variables, such as the chemical properties of water and the nature of the substrate, require consideration. Our study remains preliminary since our database needs to be further developed by completing our database with other insect groups and conducting phylogeographic research.
Acknowledgements Chevelie Cineas received funds from the  French Embassy in Haiti  in the context of a co-tutelle PhD thesis between the  University of Lyon 1 (France)  and the  State University of Haiti (Haiti). This work was also performed under the auspice of the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL)  within the  program 'Investissements d'Avenir'  operated by the  French National Research Agency (ANR). This article was previously edited for proper English language, grammar, punctuation, spelling and overall style by two highly qualified native English-speaking editors at American Journal Experts (certification verification key:  747A-2FFD-117D-C8B5-6BC3 ).
10. Flow cytometry analyses (PyroninY, MitoTracker) were performed on a BD LSRFortessa cell analyzer using DIVA software for data collection and FlowJo software for data analysis. The list of antibodies is available in Supplementary Table  1. Cell size measurement. Quiescent and activated satellite cells were sorted based on α7-INTEGRIN + , VCAM1 + , CD31 neg , CD45 neg , SCA1 neg , and CD11b neg. Singlecell brightfield images were captured on the Amnis ImageStream XMk II and analyzed on the IDEAS Software. PyroninY and MitoTracker staining. About 40 nM PyroninY (Santa Cruz) and 40 nM MitoTracker Deep Red (Thermo Fisher) were added to the muscle digests and incubated for 30 min at 37 °C in water bath. Then, muscle digests were washed and stained with the antibodies for gating the satellite cell population (α7-INTEGRIN + , VCAM1 + , CD31 neg , CD45 neg , SCA1 neg , and CD11b neg ).
Library construction was performed with 20 ng of input total RNA using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina-polyA mRNA workflow (New England Biolabs). The libraries were sequenced with a NextSeq 500 High Output 75 cycle kit (Illumina). RNA-seq reads were mapped to transcripts from GRCm38_GENCODE. vM19 using salmon v0. 13. 1  66. Data were loaded into R using the tximport library and the gene/count matrix was filtered to retain only genes with five or more mapped reads in two or more samples. Differential expression was assessed using DESeq2  67. PCA was performed using the DESeq2 plotPCA function and rlogtransformed count data. Pearson correlation between means of CPM normalized expression for each replicate group was calculated. Expression differences were calculated using the lfcShrink function, applying the apeglm method (v1.
D1 inverted microscope equipped with an EC Plan-Neofluar 10×/0. 3 Ph1 M27 objective and stitched together using Fiji software (http://fiji. sc/Fij). Other immunofluorescence pictures were taken with a Zeiss Axio Observer. D1 inverted microscope equipped with either a Plan-Apochromat 20 × /0. 8 M27 objective or a Plan-Apochromat 63 × /1. 4. Oil DIC M27 objective. For GLI3 localization, images were taken with a confocal Zeiss LSM 880 AiryScan inverted microscope quipped with a Plan-Apochromat 63 × /1. 4. Oil DIC M27 objective. Images were processed and analyzed with Zen and FIJI software. In situ force measurement. Muscle force measurements were performed on an Aurora Scientific 300C-LR-FP dual-mode muscle lever system equipped with a 1 N force transducer and 1 cm lever arm. Electrical stimulation was performed using monopolar needle electrodes attached to an Aurora Scientific 701 C High-Power, Bi-Phase Stimulator.
The number of independent experimental replications is reported in each corresponding figure legend. Unless otherwise described, data are presented as mean ± SEM and p value <0. 05 was considered as statistically significant. Throughout the manuscript, level of significance is indicated as follows: *p < 0. 05, **p < 0. 01, ***p < 0. 001. The exact p values are provided in the Source Data file. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements The authors thank  Dr. Valerie Wallace  for providing the Gli3 floxed mice,  Jennifer Ritchie  for animal husbandry,  Fernando Ortiz  for FACS,  Caroline Vergette  from  StemCore Laboratories ,  Gareth Palidwor  from  Bioinformatics Core ,  Hani Jrade  and  Damian Carragher  for helping with the ImageStream flow cytometry,  Alireza Ghasemizadeh  for helping with confocal imaging,  Hong Ming ,  Ricardo Carmona , and  Pascale Muller  for technical assistance, and  Sandy Martino  for administrative assistance. C. E. B. was supported by postdoctoral fellowships from the  Ontario Institute for Regenerative Medicine (OIRM)  and the  French Muscular Dystrophy Association (AFM)-Téléthon  and is now supported by a postdoctoral fellowship from the  Fondation pour la Recherche Médicale [FRM ,  ARF201909009155 ].
A. Y. T. L. is supported by a postdoctoral fellowship from  OIRM. P. F. was supported by a doctoral fellowship from the  Canadian Institutes of Health Research (CIHR). Studies from the F. L. G. lab are supported by grants from the  Agence Nationale pour la Recherche (ANR) :  Myofuse  project [ ANR-19-CE13-0016-03 ] and  Myofibrosis  project [ ANR-19-CE14-0008-02 ], and from the  European Joint Program on Rare Diseases (EJP RD) :  MYOCITY  project. M. A. R. holds a  Canada Research Chair in Molecular Genetics. These studies were carried out with the support of grants from the  U. S. National Institutes for Health  [R01AR044031], the  Canadian Institutes of Health Research  [ FDN-148387 ], and the  Stem Cell Network.
Several open research data repositories (e. g. , Zenodo, Fighsare) are available. The site re3data is a directory of the main data repositories. More recently, some research institutes have launched their own solutions to facilitate data sharing and open data publications for their researchers and collaborators (e. g. , the Portail Data INRAE, Open Agrar). For data connected to publications, journals may impose specific repositories. In addition to data repositories, several platforms providing data services are available such as OpenAIRE and EOSC among others. To follow the FAIR principles, it is important to add a description of your data set in the data repository. This set of information describing a data set is called metadata. It is important to use the same terms when referring to the same variables.
Ontologies have been developed for livestock phenotypic traits (ATOL), for lab analyses data (JERM) and for bioinformatics analyses (Gene Ontology). After depositing your data in to repositories, you can disseminate your data via a data paper, which is an article that describes research data in a structured and readable form. Some journals accepting data papers are CABI Agriculture and Bioscience, Animal Open Space and Data in Brief. For source code, it is important to consider the purpose of sharing and to make your code easy for others to use. Storing your code as open-source makes it freely accessible, (re)usable, and improvable by anyone. You will need to provide instructions for running your code with a reproducible example and to list required software with their version details. You can provide your method as an installable library (e.
g. in Google Scholar. As well as early accessibility, preprints also allow informal feedback from peers prior to formal peer review (see Step 5) and thus may help improve manuscripts before submission to a scientific journal. The major drawback of the preprint model is the lack of formal peer review that can help safeguard the scientific quality of the posted manuscripts. This drawback can be partly circumvented by the use of open peer-review platforms (Step 5). Most journals in animal science allow preprinting (see Sherpa Romeo to check the journal policy). Preprints can be deposited in three types of platforms: (1) preprint servers like e. g. , bioRxiv, arXiv, OSF, (2) institutional repositories of which many universities and institutes have their own and (3) multidisciplinary archives like Zenodo and HAL.
There are plenty of established preprint servers for life sciences, but only very few are specifically for animal science (e. g. , agriRxiv). OpenDOAR is a directory of OA repositories. Once a server is chosen, the actual preprinting is relatively simple: upload your final document and add meta-data -that's it. It is the best practice to upload the most recent version to the preprint server (the file will be updated, but previous versions are still accessible). However, it should be noted that some platforms, such as agriRxiv, do not accept versioning at the moment. Once published in a journal, you should include the full reference and link to your journal article in the preprint version -both steps will facilitate access to the most recent content and ensure your work is cited properly. Services such as Google Scholar allow you to merge the preprint and the corresponding peer-reviewed journal papers, so they won't be duplicated in your publication list and will not bias the calculation of some metrics such as the h-index.
Diamond OA journals operate mainly at local and national levels. The Directory of Open Access Journals (DOAJ) indexes most of the journals using the OA model. In 2018, the Plan S initiative for Open Access publishing was launched. This initiative is supported by an international consortium of organisations that fund and carry out research. Plan S requires that, from 2021, scientific publications resulting from research funded by public grants must be published in compliant OA journals or platforms. The journals aligned with Plan S can be found here.
OPR can also take place after the publication of a manuscript by tools developed by journal publishers (e. g. Frontiers In) or via independent platforms. These platforms can be used for the evaluation of published articles and preprints. Some examples are PREreview, F1000Research, Review Commons, Hypothes. is and PubPeer. We encourage you to adopt OPR in your research practices as both author and reviewer. As reviewer, you can go further and align with the Peer Reviewers' Openness Initiative  (Morey et al. , 2015)  by providing comprehensive reviews of a manuscript only if it meets minimum open science requirements (e. g. , the data should be made publicly available). Keep in mind that making the identity of reviewers open is a controversial issue. For example, Early Career Researchers may not want to go public when reviewing work by more established scientists.
Step 6: Preregister your study To ensure appropriate methods are used when collecting and analysing data, test protocols and analysis plans should be preregistered  (Nosek et al. , 2018). This consists of authors depositing their hypotheses and study designs on preregistration servers (e. g. , https://osf. io/) before data collection begins. There are also specific registries for animal experiments, such as the Animal Study Registry. Preregistration can be useful to prevent questionable research practices such as p-hacking (i. e. , changing statistical methods until a p-value below 0. 05 appears) and HARKing (Hypothesising After Results are Known)  (Kerr, 1998). Both of these are highly problematic as they can mislead researchers that a biological relevant effect exists where there isn't any.
Acknowledgements We thank  Denis Bourguet  and  Thomas Guillemaud , founders of the  open science initiative Peer Community In , for their inspiring engagement in the open science movement. Rafael Muñoz-Tamayo  thanks  Eva Legras  ( AgroParisTech, France ) for her outstanding work on training and raising awareness on open science practices in the  AgroParisTech community. We thank  Eva Legras  and  Denis Bourget  for proofreading the manuscript and providing valuable feedback to improve the paper.
Frequently Asked Questions about Open Science In the animal science domain, few journals such as Animal Behavior & Cognition accept RRs (see this list with journals accepting RRs). The PCI Registered Reports is dedicated to RRs. Box 1. Questions Answers Why should I share my data? Sharing data enables building aggregated datasets in the long term which promotes methodological progress, validation of findings and reproducibility. Shared datasets can also increase the interest of others towards your work and lead to more citations and new collaborations. The development of my model was a Once your paper is published, submitting the code in an very time-consuming task, why adapted repository ensures that your results are re-usable. should I share it? Your code can also be cited. Sharing your code will increase visibility and citation of your work.
Confronting these readings with the adaptive reuse strategies developed for the OG led to rethinking of industrial heritage within particular urban planning frameworks. Two three-month fieldworks were carried out in London and Turin between February and September 2022, bringing together participant and non-participant observations by means of a field diary. Fieldwork in Beijing is still pending given the policies linked to Covid-19, although some data was collected remotely between 2021 and 2022, in particular observations and photographs as part of a collective collaboration (see author's notes). Finally, this research is also fed by a few long semi-structured interviews (1 to 2 hours) conducted with IOC members between November 2021 and April 2022, in order to better understand the main issues of each Olympic edition, the evolution of conditions for organising the OG, and the territorial impact.
Transconjugants were obtained at efficiencies of >9 × 10 -9 per exit recipient. pGhost9::ISS1 was cured from transconjugants by growth in milk at 37°C in the absence of erythromycin. (Wick et al. , 2017) , which includes Miniasm and Racon pipelines and Pilon polishing. DNA sequences were annotated using PATRIC RASTtk-enabled Genome Annotation Service  (Brettin et al. , 2015)  and/or Prokka  (Seemann, 2014)  prior to manual curation. In certain cases, HHpred was used for protein homology detection  (Söding et al. , 2005). Genomes were analyzed using Geneious Prime 2021. 0. 3. 1  Genetic organization depictions were generated with the EasyFig 2. 2. 5 software  (Sullivan et al. , 2011).
Characterization of plasmid-encoded eps gene clusters The eps gene clusters encoded on pEPS6073 and pEPS7127, designated as 6073-like and 7127-like, respectively, are depicted in Figure  1. pEPS6073 harbors 17 coding sequences (CDS) associated with exopolysaccharide production, which are highly conserved with those found on lactococcal plasmid p275B (accession number CP016700). A closely related variant of the 6073-like eps gene cluster, which we designated EpsM variant, was identified in several IFF collection strains and is described here as it resides on plasmid pEPS7158 in L. lactis DGCC7158. The EpsM variant can be distinguished from the typical 6073-like cluster by differences in a set of two glycosyltransferase (GTF) genes found outside of the contiguous eps operon. pEPS6073 contains GTF genes pEPS6073_22 and pEPS6073_24, whereas pEPS7158 contains GTF genes annotated as epsM (pEPS7158_18) and epsN (pEPS7158_19), whose products share amino acid identity to the respective proteins encoded on lactococcal plasmid pEPS352  (Knoshaug et al.
, 2007). While pEPS7158 EpsN shares about 92% amino acid identity with the product of pEPS6073_24, EpsM has no significant amino acid identity to the product of pEPS6073_22. Additionally, a partial putative polysaccharide polymerase gene resides upstream of epsM in pEPS7158 that is not present in pEPS6073 and is unannotated in pEPS352. Lastly, pEPS7158 contains two additional GTF genes (pEPS7158_20 and pEPS7158_21) upstream of the flippase (wzx). The 3′ end of pEPS7158_21 shares 96% nucleotide identity with pEPS6073_20. Of the eps genes specific to pEPS7158, only epsM and epsN are consistently found in all IFF collection strains that contain the 6073-like EpsM variant EPS (data not shown). The difference in GTF content between the 6073-like strain and its variant may significantly affect the composition of the EPS.
Acknowledgments The authors would like to thank the  Moineau lab at Université Laval  and  Sabine Schermann  for their bioinformatic support. We would also like to thank the  Electron Microscopy Lab VA-MD College of Veterinary Medicine  for their electron microscopy contributions. Finally, would like to thank  Christophe Fremaux  and  Carsten Hansen  for helpful discussions. This paper is dedicated to the memory of  Joseph Sturino.