dataesr/scientific-paragraphs-categorization huggingface dataset:
Tools To be as open as possible and to work in the most collaborative way, we decided on a FOSS toolset: PostgreSQL/Postgis to store the data and the R language to analyse and produce knowledge from it. We also used Git for source code versioning and to facilitate remote collaboration. The data are provided as a dump from a PostgreSQL/PostGIS database  (Ramsey and Blasby, 2001). In order to access the data, we had to set up a PostgreSQL server and configure it according to the indications provided with the dataset. Several views have been set up to pre-filter the data and provide stable access points for the R language. The R language (R Core Team, 2021) is a programming language for data processing and statistics with many libraries dedicated to geospatial data. R allowed us to query the database to get data, refine it to our needs, then produce data analysis, graphs and reports using Rmarkdown files.
Rmarkdown, an R package, allows to mix code in several programming languages and text in markdown for the dynamic production of graphs, tables and documents. The use of Rmarkdown is one of the recommended means for literate programming (along with Jupyter Notebooks and Emacs Org-Mode  (Desquilbet et al. , 2019) ) So it is a useful tool to either communicate progress inside our team and publish our results. Rmarkdown, as any markdown file, is a plain text file that can be easily versioned with any version control system. As our team is scattered in several places in France, we needed a tool to facilitate code sources communication, for this purpose, we used git. Git is a Distributed Version Control System (DVCS)  (Chacon and Straub, 2014). It enables collaborative and decentralized work. Using the Github online plateforme, we could communicate source code changes to each other in a desynchronized way between different collaborators present on several sites (Nantes, Lyon, Paris).
Pre-processing The raw data are provided in the form of a PostgreSQL/PostGIS dump. A server has been set up to store the data. Several SQL scripts create materialized views that perform much of the attribute and spatial filtering and provide stable access to data corresponding to defined criteria. A materialized view is a good compromise between a table and a view because it has similar response time to a table and also allows us to recompute values easily if threshold values changed. It is perfect to store data that does not change too often. Those views are the entry point for the R language to get data to analyse. The analyses were carried out in two stages: a first exploratory analysis and more deeper one. The first analysis aimed at verifying whether certain known environmental dynamics could be visible in the data set.
This first analysis brought encouraging results, we took it again in order to refine it and to extend the field of research. In order to have a better qualification of the quality of the dataset, we look if there is a relationship between the weather events in the environment and the tags filled by the user. More precisely, the indicator chosen is the ratio of users who have put a particular tag among the users who have put at least one tag in a particular condition (i. e. no wind vs. wind). The code producing the analysis and graphics are separated in several complementary documents 5. The selection criteria for the data we were going to use for our analyses were both attributive and spatial. We removed from all the analysis the tracks tagged test or indoor as we wanted only outdoor measurements.
While these two analyses share a common basis, the selection criteria were refined between both. Short tracks (duration less than 5 seconds) were discarded in both analysis. Long tracks (duration longer than 15 minutes) have been removed from the exploratory analysis. They were kept in the second analysis. In the exploratory analysis, we kept the tracks where the GPS median accuracy of the whole track was less than 20 meters. In the extended analysis, we implemented an algorithm to detect and remove outliers track points using a method from the R package CoordinateCleaner 6. This method was used to keep tracks that would have been eliminated because some of their points had aberrant coordinates, which allowed us to work with larger samples and with a better localization accuracy. In the first analysis, we decided to keep tracks that are comprised in a rather small area, the bounding box area of the track would have to be equivalent to a square of 25 by 25 meters.
This criterion was not considered in the extended analysis, but a threshold of 8 m/s (equivalent to 30 km/h) was set between 2 GPS measurements to eliminate tracks with aberrant distance between two measurements points. Although the extended analysis examined some questions on the global dataset, the results presented here are for tracks in metropolitan France. To extract data regarding France metropolitan area, we pre-filtered the data using the NaturalEarth dataset to keep tracks that are within the France country. This spatial filtering was implemented by creating a materialized view within the PostGIS database. NaturalEarth is a great dataset and is well known worldwide however it does not distinguish France metropolitan area from overseas areas. This could have introduce complications with timezone handling.
We refined the spatial filtering to France's metropolitan area using an authoritative dataset (Institut Géographique National, 2021). To facilitate the replication of the study on areas without this kind of issues, this as not been done within the database but at the beginning of the Rmarkdown document where the exploratory analysis is conducted. The Rmarkdown document establishes the connection with the materialized view containing the data from the PostgreSQL server and then performs the operations allowing to finalize data preparation and analyze the data. The data is analyzed using several Rmarkdown files that can be launched independently or not. Some files prepare additional data or calculate indicators. Others analyze the results.
Environmental acoustics results The main goal of this preliminary study was to control the quality of the tags defined by the users in a crowdsourced acoustic data and if thoses tags are sufficient to describe the user environment. The results presented here concerns tracks bearing a tag, recorded in metropolitan France between 2017 and 2020. It focuses on the proportion of a certain tag in relation to all the tags for a given period (time of day, season, etc. ). In the sample studied, it is possible to note a large prevalence of the road tag as shown in Figure  1. chatting, animals and wind tags are also well represented. To have a first idea of the quality of the tags in the dataset, we tried to see if known temporal dynamics in environmental acoustics appeared in the data. Subsequently, we seek to determine for physical phenomena such as rain and wind if the presence of tags related to them in the dataset corresponded to real measurements of rain and wind of the national weather service.
Known environmental acoustics temporal dynamics : a first axis of analysis concerns the time distribution of certains tags. The first tag we studied was the animals tag. We wanted to see if we were able to see a rise of this tag presence in the dataset close to the sunrise. Indeed as shown by Robbins (1981), bird song activity is highest in the morning, especially in the hour after sunrise. Figure  2  shows that the tag animals (associated to bird songs) is more frequent in the morning and especially at sunrise time. The second tag we studied was the road one. We also observed peaks in human activity, especially the commuting morning and evening peak times. Figure  3  shows peak times ate 9:00 AM-10:00 AM and 8:00 PM-9:00 PM. On a previous study on commuting patterns from smart card data in Beijing (China),  Ma et al.
(2017)  found peak times at 7:00 AM-9:00 AM and 5:00 PM-7:00 PM. This time lag might be due to several reasons that we did not explore: cultural difference between China and France, different mobility practices (smart card for public transportation data versus road noise data), different types of study area (Beijing metropolis and metropolitan France). Seeing those dynamics was a good sign regarding the validity of crowdsourced environmental acoustic data. However, we wanted more precise quality indicators so we looked if we were able to correlate the mention of physical events in the dataset and records of thoses events in an authoritative database. 4. 1. 2 Correlation between tags and physical events in the user environment : to determine the accuracy of the manually selected tags by the users of the NoiseCapture app, we wanted to proceed simple investigations concerning some tags related to physical events, such as the weather related ones (rain,wind), which can be verified later on.
As we know the user position and we have the timestamp of the recording, we are able to determine the weather conditions at that time and place using the services of Météo-France, the French national meteorological service. The weather data used is from France's national weather organisation Météo-France, available along with its full documentation from their website : French weather data 7 The meteorological data collected are from the nearest meteorological stations. The median distance between weather stations and our tracks is 16. 1 kilometers. The proportion of wind and rain tags seem to correlate with their corresponding physical events. In order to create those graphs, we selected scales to group the data (wind force and precipitation) and compute the proportion of tags present in those groups.
There is a strong correlation between the proportion of wind tags and the strength of wind, r(7) =. 93, p < 0. 01. The correlation between the proportion of rain tags and the amount of rain is less significative, r(6) = 0. 68, p < 0. 1. This might be a perceptual bias from the participant or because of too small groups. The Figure  4  shows that there is very few rain tags when the weather services does not record rain. The dataset we have studied is not sufficient to characterize the amount of rainfall but sufficient to qualify the presence or absence of it.
Benefits of OpenScience and literate programming Intrinsically, Free and Open Source Software (FOSS) brings many benefits to any project, whether it is a scientific one or not. The problem of licenses is greatly facilitated because they are usable by all, without discrimination and without additional cost. The question of having to acquire licenses and how to do so is no longer an issue. Another advantage, as long as one has the necessary skills, is the possibility to adapt the tool to one's needs, an important quality in an often innovative field. Free and Open Source Software favors interoperability and this aspect is important when creating complex processing chains, implementing several different tools. As shown in Section 3. 2, this study use a rather small toolset of FOSS. The reduction of the number of tools needed for the scientific production was possible thanks to the use of a programming language allowing to cover practically all the aspects of the project, whether it is the connection to the database, the statistical analyses, the production of tables and graphs as well as reports or scientific papers.
By restarting the analysis, it is possible to see if all the conditions are met to resume the work: is the necessary software present? Is the data accessible? Does the code still work after updates? For the team or the laboratory in which the project takes place, the benefits are also numerous. The work done is accessible and readable, especially if collaborative tools have been put in place. Within the framework of the analysis presented here, an initial exploratory analysis revealed certain known dynamics in environmental acoustics. This motivated the recruitment of a new team member to carry out a more thorough analysis. The problem of setting up the database did not arise again because the server used for the exploratory analysis was still functional. Nevertheless, the other technical aspects of the analysis (creation of SQL views, connection and interrogation of the database with R, etc.
Especially in times of doubt, when the information provided by studies can be contradictory. The production of knowledge in open mode, where everything is described and auditable, can help not only the scientific community to validate the soundness of the work and the results presented, but also the general public to appropriate them. For a researcher working on similar questions, a research project where the data, methods, code and even the scientific literature produced are open and documented, makes it much easier to revisit the work in order to answer one's own questions because it provides a complete example and not just final results. While being a valuable aid for reproductibility, the FOSS and literate programming with notebook tools such as Rmarkdown used in this project suffer from some limitations.
It is possible to redo the calculations by trying to to reproduce the software environment (the versions of the software and packages used are provided), but there is no guarantee that the result will be exactly the same because the computer environment (is difficult to replicate. The package R renv 8 can help to reproduce an R environment by providing functions for saving and restoring dependencies at the project level. In some cases, if this is not enough, it will be necessary to turn to lower-level solutions such as Docker 9 and Guix 10.
CONCLUSION The major objective of this study was to control the quality of the tag data in an environmental acoustics crowdsourced dataset. The first step was to find if known temporal dynamics of sound sources are present in the dataset The first results were encouraging because sound dynamics related to commuting or animal activity can be observed. In a second time, we checked the correspondence between tags related to the acoustic measurement conditions (presence of rain or wind) with the records from the national meteorological service database. It works very well with the wind with a strong positive correlation. We believe that this works with rain but that either the sample size is too small or the perception of light rain is not sufficient for the user to think about adding the corresponding tag.
As a result, the correlation between actual rainfall observations and the presence of rain tags in the database is weaker and less significant than for wind. This study shows that it is possible to observe known dynamics in environmental acoustics inthe crowdsourced NoiseCapture dataset (road dynamics related to commuting or animal activity). It also increases the level of confidence brought to this dataset by the fact that the measurement conditions indicated by the user seem to correspond to real measurement conditions (rain and wind). Some questions still need to be explored, notably those concerning the representativeness and the unbalanced nature of the samples, which are sometimes weak for certain periods. We also wanted to show that Free and Open Source Software can be used to produce scientific knowledge all along the production chain, from data collection, storage, analysis and even presentation.
By mobilizing only a few tools, it is quite possible to produce scientific knowledge in a more reproducible way. The systematic use of open source software, the provision of documented code files and a document mixing narrative, figures and code has greatly facilitated the recovery and continuation of the work presented here. And this within our own research team. We are aware that the acquisition of the necessary skills to mobilize these tools is not easy and need time but we believe that the gain is much higher than the cost. Reproductibility is an important constraint for research projects that often have limited means, nevertheless it is an investment in the future, both for the team and for the scientific community interested in the work. For the more technical parts, such as the implementation of database servers and help with the reproductibility issues, the presence of research software engineers in a team or a lab is a real plus.
https://research-data. ifsttar. fr/dataset. xhtml?persistentId=doi:10. 25578/J5DG3W The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W1-2022 Free and Open Source Software for Geospatial (FOSS4G) 2022 -Academic Track, 22-28 August 2022, Florence, Italy This contribution has been peer-reviewed. https://doi. org/10. 5194/isprs-archives-XLVIII-4-W1-2022-387-2022 | © Author(s) 2022. CC BY 4. 0 License.
Bacterial Detection DNA was extracted from C. burnetii infected cells using a DNA Mini Kit (Qiagen, Courtaboeuf, France). Bacterial load was quantified using real time quantitative PCR (qPCR) performed with specific primers F (5'-GCACTATTTTTAGCCG-GAACCTT-3') and R (5'-TTGAGGAGAAAAACTGGATTG AGA-3') targeting the C. burnetii COM-1 gene, as previously described  (36). The presence of C. burnetii within cells was also assessed by flow cytometry. Briefly, infected cells were fixed with 4% paraformaldehyde and permeabilized with 0. 1% Triton X-100 (Sigma-Aldrich). After washing, cells were incubated with a rabbit antibody directed against C. burnetii for 30 min and then with an Alexa 647 anti-rabbit antibody (Invitrogen). Data were collected on a BD Canto II instrument (BD Biosciences, Le Pont-de-Claix, France) and analyzed with FlowJo software (FlowJo v10.
BTN3A and BTN2A Surface Expression Cells were suspended in PBS (Life Technologies) containing 1% FBS and 2 mM EDTA (Sigma-Aldrich). Cells were labeled with viability dye (Live/Dead Near IR, Invitrogen), mouse anti-BTN3A (clone 103. 2) or anti-BTN2A (clone 7. 48) Abs or with the appropriate isotype control (Miltenyi Biotech). After 30 min incubation, primary antibody binding was detected with secondary PE anti-mouse antibody (Invitrogen) and data were collected on a Navios instrument (Beckman Coulter) and analyzed with FlowJo software (FlowJo v10. 6. 2).
burnetii gene CB COM-1 GCACTATTTTTAGCCG-GAACCTT TTGAGGAGAAAAACTGGATTGAGA MTB ITS CAAGGCATCCACCATGCGC GGGTGGGGTGTGGTGTTTGA and fluorochrome-labeled CD107a and CD107b (BD Biosciences). Phorbol 12-myristate 13-acetate (PMA, 20 ng/ mL) with ionomycine (1 µg/mL) were used as positive control for Vg9Vd2 T cell activation. After 4 hours, cells were harvested and stained with fluorochrome-labeled TCR-specific mAbs (Miltenyi Biotec) and a viability marker (Live/Dead Near IR, Invitrogen). The degranulation was evaluated by flow cytometry as the percentage CD107a/b + cells in the gd T cell population (Supplementary Figure  2 ). Data were collected on a Navios instrument (Beckman Coulter) and analyzed with FlowJo software (FlowJo v10. 6. 2).
Cytotoxicity Assay Monocytes were labeled with 10 µM Cell Proliferation Dye eFluor ® 670 (Invitrogen) and then co-cultured with Vg9Vd2 T cells at E:T ratio of 1:1 in presence of mouse anti-BTN3A mAb (clone 20. 1) at the indicated concentrations. After 4 hours, cells were stained with CellEvent Caspase-3/7 Green (Invitrogen) to identify dead cells. The cytotoxicity was assessed by flow cytometry as the percentage of Caspase 3/7 + cells in the target cell population (Supplementary Figure  2 ). Data were collected on a BD Canto II instrument (BD Biosciences) and analyzed with FlowJo software (FlowJo v10. 6. 2).
This is the case in a trial in cancer patients where the approach is to activate Vg9Vd2 T cells by targeting BTN3A (NCT04243499, ImCheck Therapeutics, Marseille, France)  (57, 58). In addition, we have also explored the effect of the 20. 1 mAb in the case of SARS-CoV-2 infection. By activating the Vg9Vd2 T cells, 20. 1 mAb may affect intracellular SARS-CoV-2 replication in vitro in infected cells (submitted manuscript). Future studies should be conducted to elucidate the detailed mechanisms of protective Vg9Vd2 T cell activation and how precisely BTN3A is involved in infections. These results highlight that the BTN3A agonist antibody could represent powerful therapeutic tool in infections to overcome the imbalances in immune responses observed in some patients and open new perspectives in Vg9Vd2 T-cell-based immunotherapies in infectious diseases.
These same parental traits have also been associated with children's internalising behaviours  (Crawford et al. , 2011; Puff & Renk, 2016). However, to our knowledge, only one study has assessed associations between parental biologically based personality traits based on ANPS profiles and school-aged children's behaviours  (Orri et al. , 2019). This study, based on data from the same cohort as the current study, utilised latent classes to capture broad parental ANPS personality profiles and studied associations with parent-reported child internalising and externalising behaviour. The three latent parental ANPS profiles were low negative emotions (lower levels of ANGER, SADNESS, and FEAR, greater levels of PLAY, SEEKING and CARE), balanced (average levels of all subscales) and high emotional (greater levels of ANGER, SADNESS, FEAR and CARE)  (Orri et al.
Participants Five hundred and fifteen families who had taken part in a larger perinatal survey were invited to participate in the longitudinal community-based study 'Étude de Milieu de Garde' (EMIGARDE; translation: Study on Child Care Services; for more information on initial recruitement see  Côté et al. , 2013). Children were born between June 2003 and April University Hospital in Montréal, Québec. During the home visits, separate interviews took place with mothers and their child, and children's self-reported data were obtained using the Berkeley Puppet Interview (described below). Furthermore, mothers and fathers completed questionnaires separately, which included the ANPS measure and questions that pertained to parenting practices. Families received compensation for participation in the study.
Socioeconomic status Previous literature has reported associations between socioeconomic status and children's behaviour difficulties. More specifically, lower socioeconomic status has been linked to higher scores for externalising and internalising behaviours in children  (Keiley et al. , 2000). We considered the following socioeconomic indicators in this study, which were created using data collected from participants in 2011-2012. Maternal education was defined by the highest level of completed education and classified as secondary (secondary education or less), college (college or intermediate vocational education), or higher education (university). Single parenthood was defined as living in a single parent family at the most recent point of data collection. Family income, defined as the total income before tax for all household members during the last 12 months, was dichotomised with a cut off of $40,000 CAD per year, based on a low-income variable generated by Statistics Canada in 2013 for a family of four in 2011 and 2012, when the data collection took place (Statistics Canada, 2013).
Statistical analysis approach In a first step, multiple linear regression analyses were performed using the Statistical Package for the Social Sciences (SPSS 27. 0) to examine links between the six standardised ANPS subscales and both child internalising and externalising behaviour. Separate regression analyses were conducted for mothers and fathers, for child externalising and internalising behaviour, separately, for a total of four regression models. Each model contained the six parental ANPS subscales (mother or father) to examine the unique contribution of the subscales for child behaviour (internalising or externalising difficulties). These exploratory regression analyses included moderation by child sex through backwards stepwise multiple regression in addition to all covariates (i. e.