dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). As a result, MacaqueNet is a meticulously curated database with data readily available for future comparative studies  (Borries et al. , 2016; Gomes et al. , 2022). MacaqueNet extends beyond mere data sharing. By linking researchers across different research labs and institutions, MacaqueNet facilitates a global exchange of ideas and fosters the development of new research projects. To provide an opportunity for all the current data contributors to meet, discuss their research and learn about ongoing projects, we organised a virtual global symposium 'Weaving the MacaqueNet' in November 2021. During the symposium, future directions for MacaqueNet were discussed and agreed on. As a first step in the establishment of an infrastructure that permits researchers to communicate and coordinate their research, we set up a website (https://macaquenet.
github. io), a mailing list and a group messaging channel on Slack. The second 'Weaving the MacaqueNet' symposium was held in August 2023 at the Joint Conference of the International Primatological Society and Malaysian Primatological Society, where the MacaqueNet community got the opportunity to meet in person. At that meeting, we discussed the expansion of MacaqueNet to include more types of data, thereby increasing MacaqueNet's ability to address a range of questions on the biology of social behaviour. To promote transparency, we also agreed to make all meetings associated with MacaqueNet open to all MacaqueNet members, in addition to disseminating a quarterly newsletter outlining new projects and discussion points. At the time of publication, four collaborative projects are using MacaqueNet data: 1) a project investigating the links between social diversity and social complexity; 2) a project exploring the socio-ecological drivers of variation in social relationships; 3) a project testing the impact of hierarchy steepness on rank-related benefits and 4) a project looking into predictors of intersexual dominance.
New projects can be easily proposed, and data requested with a brief project proposal via the website (https://macaquenet. github. io/database/). The proposal is forwarded to the data contributors, who assess whether they can provide their data for the proposed project and determine their usage terms (see MacaqueNet Terms of Use in the supplement). New types of data to be added to the database can also be suggested on the website. These ideas and their potential implementation are discussed at each MacaqueNet symposium.
To expand the scope of the MacaqueNet database, efforts are underway to include additional data types such as life-history, morphological, endocrinological, and genetic/genomic data. The MacaqueNet community is enthusiastic about sharing these data, which will enable new developments in the study of social relationships, their functions, and their underlying mechanisms. We envision that MacaqueNet will have significant potential to shed light on four main areas of research on social evolution. First, the genus Macaca exhibits pronounced variation in ecology and social structure, but stable social organisation across species  (Thierry, 2007). This makes it possible to quantify between-and within-species variation in social structure  (Balasubramaniam et al. , 2012; Neumann & Fischer, 2023)  and explicitly test the social and ecological drivers of this variation.
Discussion In this paper, we advocate for the collaborative building of FAIR cross-species comparative databases as a critical tool in the study of the biological drivers of social relationships. Understanding the substantial variation in the social relationships animals form requires comparing taxa with distinct life histories, ecology, and interlinked evolutionary histories, all of which may be key drivers of social variation  (Kappeler et al. , 2019; Lukas & Clutton-Brock, 2018 ). Yet, despite decades worth of social behavioural data  (Sheldon et al. , 2022) , substantial challenges have impeded systematic comparative research, one of which is the creation of truly comparable databases  (Borries et al. , 2016). We propose that large-scale collaborative efforts, where data and expertise are shared among researchers, can provide a solution to this issue and help facilitate reliable cross-species comparisons.
To inspire and facilitate more collaborative efforts in the biology of social behaviour, we share our experience in developing MacaqueNet and its cross-species database. Building on the foundations of previous big-team projects that have been instrumental in shaping MacaqueNet (including but not restricted to SPI-Birds:  Culina et al. , 2021 and ManyPrimates: ManyPrimates et al. , 2019) , we highlight three substantial advantages for the scientific community to join forces and pool their data into FAIR databases. First, by creating an enduring infrastructure for collaboration, big-team science efforts represent a crucial interface linking existing data and data users  (Culina et al. , 2021). They ensure that valuable data, which might have otherwise remained largely unused, are easier to discover, repurpose, and synthesise  (Gonzalez & Peres-Neto, 2015; Purgar et al.
, 2010). Creating a community rather than just a database also offers data contributors networking opportunities and access to others' data, fostering collaborative research possibilities. Third, social behavioural data are particularly complex, with many researcher degrees of freedom involved in how the data are defined and what methods of observation are used  (Borries et al. , 2016). MacaqueNet demonstrates the benefits of starting small, with a single taxon that exhibits similar social organisation and behaviour across species. This approach allowed us to focus on core issues, such as data comparability and standardisation, and identify potential solutions to address those issues. For instance, MacaqueNet has spurred the creation of BISoN, a novel Bayesian framework for inference of social networks  (Hart et al.
2023). Two other projects have arisen to address challenges in comparative social network analysis that were long recognized but for which MacaqueNet has provided well-defined questions and the data necessary to address them. Our experience also emphasised the importance of standardising data through direct interaction with data contributors, thus ensuring clarity around the intricacies of the data and averting inadvertent misinterpretations. These developments, along with the established infrastructure, such as the guidelines, workflow and code for dealing with data sharing, can pave the way for databases of larger taxonomic scope and including more types of data. This will be key to get a comprehensive understanding of the development, regulating mechanisms and life-time fitness consequences of social relationships framed in the context of relevant socio-ecological pressures and phylogenetic history.
The rise of collaborative databases in a variety of fields is a testament to the enthusiasm of researchers to join forces and share data and resources towards comparative and interdisciplinary studies, an emerging and promising way of doing science  (Coles et al. , 2022). Big-team endeavours like MacaqueNet facilitate the exploration of innovative and comparative questions no single research lab could address individually and make testing the reproducibility of research findings across contexts possible. While the creation of databases on social behavioural data comes with substantial challenges, we truly believe this is the way forward to harness the incredible comparative potential of the wealth of existing social behavioural data. We hope that MacaqueNet, along with similar endeavours, can catalyse large-scale collaborative research into the biology of social behaviour.
A second folding was conducted before placing the wipe in a glass bottle. Sampled surfaces were measured by the investigators using a template, a caliper, or a measuring tape. Field blanks were prepared by exposing moistened gauze in situ for 1 min to air. During sampling, an investigator compiled observational data on the selected surfaces (in terms of their size, material, how often they were used, and when and how they were last cleaned) in a structured observation notebook, with input from household members if necessary. All wipes were individually placed in separated, cleaned glass bottles and placed in an electric cooler at -18 • C immediately after sampling. They were transported to the laboratory within four hours and stored at -20 • C before analysis  [15].
Pesticides Selected Active substances were selected a priori according to several criteria. First, they had to be conventional pesticides (without microorganisms or natural products) authorized for use in France in 2019 in the wine industry as mentioned in the European Union pesticide database  [17]  and in the French E-Phy database  [18]. In total, 75 active substances met these criteria. The active substances were then prioritized based on the tonnages sold in the Gironde department in 2017, the date of the last update of the National Bank of Agricultural Crop Protection Product Sales (BNVD-Banque nationale des ventes de produits phytosanitaires agricoles)  [19]. The frequency of application during the year and their duration of use, as determined by expert opinion, were taken into account.
In the final selection phase, we used analytical criteria to select the six substances: benalaxyl, boscalid, cymoxanil, pyraclostrobin, tebuconazole, and trifloxystrobin. These pesticides are not intended for domestic use, except for tebuconazole, which is also used to protect wood against woodeating insects  [20]. The main information about properties and use of the six pesticides, obtained from the Pesticide Properties Database (PPDB) website  [21]  and the BNVD  [19] , is presented in Table  1.
Data Management and Statistical Analysis The information collected during the home visit and the results of the pesticide analysis were entered into a database; descriptive statistics were performed using R version 4. 1. 2 [22]. For all surface samples and for each substance, we calculated the detection and quantification frequencies per household and for all houses combined. We also calculated the surface loading defined as the quantity of pesticides related to the surface area, expressed as mass per unit area (ng/m 2 ). For individual measurements, we considered the mass of pesticides quantified per volume (liter of water) or per sampling time (patches). Next, we plotted the distribution of the surface loadings per pesticide based on several parameters: location in the house, type of surface sampled, and the time and method of the last cleaning.
Assessment of Acceptability In order to assess the acceptability of our protocol, an interview was scheduled with a member of the household who was present during the initial visit a few months after the data collection. The semi-structured interview was conducted by telephone using a guided questionnaire consisting of nine items. It included brief questions about the planning and execution of the home visits (organization, duration, and participant involvement), potential disruptions during the visits or sampling, whether the study was perceived to be useful, reasons for the study, and reproducibility of the protocol. An open-ended question about possible provisions that could improve the participation of future subjects was also included at the end of the interview.
Cows were fed using an individual gate system (American Calan, Northwood, NH, USA), and feed intakes were recorded daily. Diets were formulated using the Cornell Net Carbohydrate and Protein System model, contained in the NDS Professional ration formulation software (Nutritional Dynamic System; v. 6. 55; RUM&N) to meet 100% of National Research Council (NRC) nutrient requirements  [21]. During the dry period, cows were housed in a bedded pack pen, and 3 d before the expected calving date, cows were reallocated in individual pens bedded with straw until parturition. After parturition, cows were moved within 2 h to an individual chute and milked with a porta-milker vacuum pump (Nasco, Fort Atkinson, WI, USA; Cat. no. Z15664N). On d 3 postpartum, cows were moved to a free-stall barn. A retrospective analysis was performed in this subset of cows, using the postpartum β-hydroxybutyrate (BHB) data measured on whole blood collected before morning feeding at 1, 3, 5, 7, and 9 DIM with the precision Xtra (Abbott Diabetes Care Inc.
Calf Enrollment Criteria As the cows were giving birth, a subset of calves from each group (YC, n = 8; Control, n = 7) was randomly selected to be followed until weaning. After completing the data collection period (from 0 to 56 days of age), each calf was classified based on the respective dam's ketotic condition. This classification resulted in 6 Holstein dairy calves classified as SK, and in order to match the same number of animals in the NONKET group, only 6 calves out of 9 NONKET calves were randomly selected. Calves enrolled in this study were born from October 2018 to April 2019. Variability of calf pairs was minimized by applying the following inclusion criteria: (1) calving difficulty score < 3, where 3 represents minimum assistance, but no calving difficulty, (2) calf birth weight ≥ 32 kg, (3) single calf, (4) colostrum quality ≥ 21% brix refractometer, and (5) 3.
After incubation, 600 µL of 96% molecular ethanol was added and vortexed. The mixture was transferred into a QIAamp mini spin column, and the subsequent steps were performed according to the manufacturer's procedures (Qiagen, Hilden, Germany). DNA quantity and purity were measured using a Nanodrop spectrophotometer (ND 1000, Nanodrop Technologies, Inc. , Wilmington, DE, USA). MiSeq 2 × 300 paired-end reads from amplicons targeting the V1-V3 region of the bacterial 16S rRNA gene were generated by the University of Minnesota Genomics Center. Overlapping raw forward and reverse reads from the same flow cell clusters were assembled into contigs using the 'make. contigs' command from MOTHUR (v 1. 44)  [29]. Unless specified otherwise, all other sequence data analysis steps were performed using custom-written Perl scripts.
OTUs were screened for DNA sequence artifacts using the following methods. Chimeric sequences were first identified with the 'chimera. uchime'  [34]  and 'chimera. slayer'  [35]  commands from the MOTHUR (v 1. 44. 1) open-source software package. Secondly, the integrity of the 5 and 3 ends of OTUs was evaluated using a database alignment searchbased approach; when compared to their closest match of equal or longer sequence length from the NCBI (National Center for Biotechnology Information) 'nt' database, as determined by BLAST  [36] , OTUs with more than five nucleotides missing from the 5 or 3 end of their respective alignments were discarded as artifacts. Single-read OTUs were subjected to an additional screen, where only sequences that had a perfect or near-perfect match to a sequence in the NCBI 'nt' database were kept for analysis, i.
e. , the alignment had to span the entire sequence of the OTU, and a maximum of 1% of dissimilar nucleotides was tolerated. After the removal of sequence chimeras and artifacts, taxonomic assignment of valid OTUs was performed. All OTUs were classified at the levels of Phylum and Family using RDP Classifier  [37]. For select OTUs, BLAST queries were also used to identify their respective closest valid relatives  [36]. Alpha diversity indices (Observed OTUs, Chao, Ace, Shannon, and Simpson) were determined using the 'summary. single' command from MOTHUR (version 1. 44. 1)  [29]  on a dataset rarified to 5000 reads for each sample. Principle Coordinate Analysis (PCoA) for beta diversity was performed using the same rarefied dataset by first determining Bray Curtis distances with the 'summary.
Statistical Analysis Data were analyzed with the Proc MIXED procedure of SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). Fixed effects in the model were group (G), Time (T, day or week), and their interaction (G × T). The random effect was a calf within a group. The authors acknowledge that the low sample size (n = 6/group) of this study could have impaired the statistical power. However, the stringent criteria applied to enrolled calves in this study were necessary to reduce additional noise in our data. Additionally, this type of maternal-neonatal experiment is difficult to perform; therefore, a similar low sample size has been used in other studies  [24, 38]. Repeated measured data for BW, WH, ADG, starter intake, fecal score, respiratory score, and rectal temperature were modeled, selecting the variance-covariance structures with the least Bayesian information criterion value among compound symmetry, autoregressive, or heterogeneous autoregressive.
Formerly considered as part of "junk DNA", pseudogenes are nowadays known for their role in the post-transcriptional regulation of functional genes. In addition, their identification allows a better understanding of gene evolution in the frame of multigenic families. Despite this, there is, to our knowledge, no fully automatic user-friendly software allowing the annotation of pseudogenes on a whole genome. Here, we present Pseudo-Gene Retriever (P-GRe), a fully automated pseudogene prediction software requiring only a genome sequence and its corresponding GFF annotation file. P-GRe detects the sequences of the pseudogenes on a whole genome and returns to the user all their genomic sequences and their pseudo-coding sequences. The ability of P-GRe to finely reconstruct the structure of pseudogenes also allow to obtain a set of proteins virtually encoded by the predicted pseudogenes.
, 2006) , two software commonly used for pseudogene prediction, compares protein sequences to DNA sequence database in both DNA orientations using the tfasty local alignment algorithm  (Pearson & Lipman, 1988). If a large number of pseudogene prediction software exist, many are specific to a type of organism, such as Pseudofinder  (Syberg-Olsen et al. , 2022)  and Psi-Phi  (Lerat & Ochman, 2004)  which are dedicated to prokaryotes. Others target specific types of pseudogenes, such as PPFINDER  (van Baren & Brent, 2006)  and PΨFinder  (Abrahamsson et al. , 2022)  which are made to predict pseudogenes originating from transcript retrotransposition events. Software able to work on any type of organisms and pseudogenes are rarer, and often produce very different outputs: some can approximate a protein or a peptide virtually encoded by the pseudogenes, while others simply return the sequence of the pseudogenes without worrying about their pseudo-coding structure.
Most generate their own results file in TSV format and surprisingly, to our knowledge, none of the software returns its results in the GFF or GTF reference formats mainly used for structural genome annotation. Last, these software are often not very user-friendly, requiring a tedious preparation of the data and the organization of the working directories according to precise instructions. Pseudo-Gene Retriever (P-GRe) allows the prediction of the positions of pseudogenes on a whole genome as well as the precise reconstruction of their structures in pseudo-coding exon. P-GRe only requires the genome sequence and the corresponding structural annotation in GFF format. It is therefore optional to provide all the protein sequences, as P-GRe can generate them from genome and annotation files. P-GRe returns the set of genomic sequences of the pseudogenes, and also the set of pseudo-coding sequences.
P-GRe Like most pseudogene prediction software, P-GRe proceeds in two steps, the first consisting in finding the approximate position of the pseudogenes and the second in refining their structure (Figure  1 ). For the first step, P-GRe uses the GFFread software  (Pertea & Pertea, 2020)  to generate the set of proteins from the genome and the annotation file provided by the user. These same files are used for hard masking annotated gene sequences on the genome using maskfasta from the BEDTools suite  (Quinlan & Hall, 2010). The generated proteins are then locally aligned to the masked genome using the tblastn algorithm  (Altschul et al. , 1990) , with the options -seg 'yes', -soft_masking True, -db_soft_mask dust,outfmt 6, -evalue 0. 01, -word_size 3, -gapextend 2 and -max_intron_length 10000.
Quality of predictions and sensibility The sensitivity of P-GRe was tested using annotation, proteome and genome from A. thaliana available on the Ensembl database (TAIR10). Among the 924 known A. thaliana pseudogenes, those that overlapped pseudogenes predicted by P-GRe over at least 60% of their length were considered found. Note that this sensitivity metric and the data used for prediction are similar to those used by  Xiao et al. (Xiao et al. , 2016) , which achieved 78. 9% and 81. 3% sensitivity with Shiu's pipeline and PseudoPipe respectively. A similar method was used to identify the predicted pseudogenes that corresponded to transposable elements (TEs, annotated "transposable_element_gene" or "transposable_element" on the TAIR10 annotation). In addition, the sequences corresponding to the elements annotated as "transposable_element_gene" were translated and the sequences virtually encoded by the pseudogenes predicted by P-GRe were locally aligned with these TE sequences using the blastp algorithm.
In addition to sensitivity, the quality of the predictions was also evaluated. All pseudogenes found were aligned locally against the entire proteome of A. thaliana using the blastp algorithm, keeping for each pseudogene only the best alignment. From these local alignments, percentage of identity, hit coverage, and total coverage (from the start of the first hit to the end of the last hit) were retained. Each protein sequence that lead to a best alignment was then semi-globally aligned with their matching pseudogene protein sequence using the pairwise2 module, with gap opening and gap extension penalties of 0. 5 and 0. 1, respectively, and with the BLOSUM62 substitution matrix. From these semi-global alignments, the alignment scores were retained. In the rare cases where a pseudogene predicted by P-GRe overlapped several annotated pseudogenes, the score for each of the TAIR10-annotated pseudogenes was set to the sum of their scores.
Acknowledgements The authors are thankful to the  Paul Sabatier-Toulouse 3 University  and to the  Centre National de la Recherche Scientifique (CNRS)  for granting their work. SC is the recipient of a fellowship from the  "École Universitaire de Recherche (EUR) "  TULIP-GS  ( ANR-18-EURE-0019 ). This study is set within the framework of the " Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
The objective of this taxonomy is to help investors, companies, issuers, and project developers implement a transition to a low-carbon, resilient, and resource-efficient economy. This taxonomy distinguishes between two types of substantial contributions to adaptation objectives: "adaptive activities" (i. e. , projects deployed to respond to a particular situation) and "activities enabling the adaptation of an economic activity" (i. e. , projects that are intended to foster the adaptation of an entire sector of activity)  [28, 29]. As demonstrated by  Boutang et al. (2020)   [2] , this type of classification can be built from the observation of NDC documents as well as climate databases. We update this taxonomy here according to issues and projects revised at the end of 2022.