dataesr/scientific-paragraphs-categorization huggingface dataset:
This generator would then create novel samples that follow the same implicit rules as the original patients in the cohort. This would allow the sharing of the cohort's general information, which could be an important resource for research teams, without revealing the identity of any one person in it. There are many other potential use cases that one could imagine for realistic synthetic genomes generated by AI. Creating a digital twin of an individual or a population could be a useful asset for sharing or experimenting on. Augmenting already available datasets of genomes might lend more robustness to certain research projects. Finally, the autoencoding of genomic data and its accurate reconstruction is a potential method of processing these samples in a secure manner, through federated learning or homomorphic encryption.
As for generative models, these past years have shown remarkable progress in stable diffusion-based models that allow for detailed illustrations based on user specifications. Although these models are based on convolution neural networks, which are particularly well suited for processing images, which we do not use, it would be interesting to study their application to genomic datasets. Evaluating the realism of a synthetic genome is a difficult task, in part because no standardised tools or methods exist for this purpose, and even more so for a synthetic population. The criteria proposed here -PCA tiling overlap score, Chromopainter metaprofile correlation and edit score -all highlight various aspects of a simulated population that are desirable. To go further, it could be interesting to investigate the biological relevance of mutations contained in these simulated genomes and seek to measure degrees of similarity and difference using variations with known biological meaning.
0190312_biallelic_SNV_and_INDEL/ Each donor genome was separated into haplotypes. Mutations on chromosome 1 were filtered to only include those present in at least three of these haplotypes, so as to remove data with little detectable correlation with the rest of the dataset. Contiguous bins of a set number of mutations were then created from this list of variants. Crossing over data was obtained from a high definition map of recombination rates throughout the human genome. Ranges for chromosome 1 were extracted, and those with a recombination rate of <=5 cM per Mb were removed to eliminate noise. The remaining ranges were reduced to their centres and sorted by recombination rate. Any low-intensity ranges within 5kb of a higher intensity range were removed. This resulted in approximately 2500 hotspots across all of chromosome 1.
There is a 40% dropout rate in between each hidden layer, for the encoder and the decoder. Bottleneck size is defined as input size divided by 100, rounded up, and the size of the hidden layers is calculated using geometric spacing. Its loss is measured as the sum of classical reconstruction loss and the Kullback-Leibler divergence  (Kullback and Leibler 1951) , to ensure a locally continuous latent space. The training process for both these model architectures uses a training set and a validation set, with an 80/20 split. The models are trained while monitoring loss on the validation dataset, and when this value increases (with a patience of 10 epochs, or 30 when using sigmoid and tanH activation due to slower convergence), training is halted and the weights of the model are restored to its best performing iteration.
Chromopainter We estimated the ancestral diversity of our simulated genomes using Chromopainter, which attempts to assign sections of donor genomes to query genomes. We use this as a verisimilitude measure, as our simulated genomes should present approximately the same profile of crossing overs as the reference genomes. We applied this tool to a reference query dataset, composed of three haplogenomes from each subpopulation listed in the 1000 Genomes Project phase 3 supplementary information, and to 100 randomly chosen simulated haplogenomes, for three genomic subsections. The donor dataset is composed of the 1000 Genomes Project individuals, without the members of the reference query dataset.
Code availability The main scripts used during this project for data preprocessing, building ML models and the custom training loop for the WGAN model, are available at this github repository: https://github. com/callum-b/H2G2/ Data Mutation data for 2504 individuals as well as donor information comes from the 1000 Genomes Project (phase 3), realigned onto HG38 in (Lowy-Gallego et al. 2019). It was downloaded here: http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/data_collections/1000_genomes_project/release/2.
of drift of each mutation for different autoencoder models. Average drift for each model is also included. b) Scatter plots of PCAs for different datasets as described for figure 2b, with each row illustrating two different sections. All genomic subsections studied in detail that were not shown in fig 2b are shown here. Supplemental fig 3 Scatter plots of PCAs for different datasets as described for figure 3a, with each row illustrating two different sections. All genomic subsections studied in detail that were not shown in fig 3a are shown here.
Acknowledgments We thank the  Interdisciplinary Thematic Institute IMCBio , the  IdEx Unistra , the  SFRI-STRAT'US  project,  EUR IMCBio  under the framework of the  French Investments for the Future Program, AFM-Téléthon strategic program , the  Université franco-allemande and Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation.
Funding This work was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ),  SFRI-STRAT'US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ). Additional funding was delivered by  INSERM ,  CNRS ,  Unistra ,  IGBMC ,  AFM-Téléthon strategic program   24376  (to D. D. ),  INSERM  young researcher grant (to D. D. ). J. R. was supported by the  Programme   CDFA-07-22  from the  Université franco-allemande and Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation , and R. S. by the  Interdisciplinary Thematic Institute IMCBio.
The G395H data had 2 integrations for each of the 4 dithers; the G235H data had 4 integrations for each of the 4 dithers. Photons are accumulated continuously during an integration. However, users may delete groups from an integration when starting from the uncalibrated raw data (uncal) to reduce issues with detector saturation. As discussed below, we utilized both 1-group data cubes (removing the second group) and 2-group data cubes in our analysis. For more information, see https://jwstdocs. stsci. edu/understanding-exposure-times. The Observatory Scheduling Software used was version 8. 4. 14. The raw (uncal) data were calibrated with the experimental pipeline version 1. 8. 3. dev69+g67b11ff5, and CRDS (Calibration References Data System) files CRDS -VER = 11. 16. 16 & CRDS -CTX = jwst -1023.
Units The NIRSpec calibration software produces calibrated spectral radiance cubes, L ν , in MJy/sr. We converted the data into other units for easier comparison with previous data. We converted our image data cubes into photon spectral radiance, L q , in photons/s/cm 2 /μm/sr via = ℎ 2 = × 1. 51 × 10 9 ∕ , (1) where S MJy = 10 -17 erg/s/cm 2 /Hz, hc/λ is the photon energy, h = 6. 63 × 10 -27 erg s, and c/λ 2 converts from power per unit frequency to power per unit wavelength, with wavelength λ in μm. We also constructed a second data cube F λ in erg/s/cm 2 /μm via = 2 × = × 3. 0 × 10 -3 × ∕ 2 , ( 2 ) where c = 3. 00 × 10 14 μm/s (same length unit as wavelength), and p sr = 2. 35 × 10 -13 , the area of a 0. 1″ pixel in steradians. The latter units were then easily converted into a distance-independent radiant flux, GW/μm/sr, by further multiplying the images by Δ 2 × 10 -3 /10 9 , where Δ is Io's distance from JWST in m.
The total intensity of each of the two volcanoes was determined by integrating the intensity in a circular aperture with a radius of 1 pixel, using the apphot. pro program in IDL (https://www. stsci. edu/∼mperrin/software/ sources/apphot. pro), after increasing the size of each image frame in the data cube by a factor of 5 to create sub-spaxels at a 0. 02″-sized grid. We choose to look at aperture-summed spectra instead of single-pixel spectra in order to suppress the wiggles in individual spaxel spectra due to an undersampling of the point spread function (PSF) (examples of wiggles are still visible in the G395H spectra in the bottom row of Figure  6 ). The intensity of the volcanoes was corrected for the loss of flux due to the extent of the PSF by measuring the intensity of the star P330-E  (Bohlin & Landolt, 2015)  taken with the same grisms and the same small circular aperture in JWST program #01538.
The Keck images were reduced and the flux densities of these volcanoes were determined in the same manner as those published by, for example,  (de Kleer & de Pater, 2016; de Pater et al. , 2014). Those images captured the two volcanoes Loki Patera and Kanehili Fluctus (as well as several others), as shown in Figure  5 , and summarized in Table  2. On August 16 and September 12 Loki Patera was detected with an intensity of ∼12-15 GW/μm/sr at 4. 67 μm and ∼2. 5-3 GW/μm/sr at 3. 67 μm. These intensities were corrected for the emission angle (ϵ) effect (i. e. , correcting for the foreshortening effect of a surface area near the limb) by dividing the observed values by cos(ϵ). On both dates, Loki Patera is fainter than the JWST measurements, which were obtained ∼2 months later. Note, also, that we did not correct the JWST data for the emission angle effect in this graph to better show the observed spectra relative to Io's total intensity.
In addition, so close to the limb, topography may affect the observed intensity, though likely would result in too low rather than too high values (de Kleer & de Pater, 2017). A correction for the emission angle would increase Loki Patera's intensity over its entire spectrum by a factor of 2. 6, bringing it almost up to Kanehekili Fluctus' spectrum. (The emission angle effect for Kanehekili Fluctus would increase its intensity by ∼10%). Kanehekili Fluctus was imaged with the Keck telescope on August 15 and September 07, and is on the West limb on September 12. The Keck values for this volcano are also much fainter than what was measured by JWST. This compilation of data demonstrates that JWST captured Io during an extremely active period: The intensity 1/4 , normalized to 100 (i. e. , the maximum intensity 1/4 is set equal to 100), is superposed on a USGS map of the satellite (https://astrogeology.
, 2001). The most up-to-date Loki Patera graph of intensity versus time (de Kleer &  Rathbun, 2023)  shows that the volcano's last brightening event stretched over April-September 2021. Based on the 440-540 day period, the next brightening event would have been expected to start in August-October 2022. The Keck observations from August and September 2022 show that it was not particularly active. The JWST observations from mid-November 2022 revealed a much higher intensity. Based upon the quasi-periodic behavior of Loki Patera's brightening events, it is quite likely that JWST detected Loki Patera's most recent brightening event. Since it is close to the limb, it is difficult to determine a precise value for Loki Patera's intensity, but if we take the data at face value and correct for the emission angle effect (factor of 2.
Acknowledgments This work is based on observations made with the  NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with program #1373 (Observation #24), which is led by co-PIs  Imke de Pater  and  Thierry Fouchet  and has a zero-exclusive-access period. We thank  Julie Rathbun  for providing the digital data for Loki Patera's quasi-periodic behavior in 2020-2022, as well as  John Spencer  and  Jani Radebaugh  for constructive comments on the paper. IdP, MHW, EMM, CM, PMF, MS, MST are in part supported by the  Space Telescope Science Institute  grant nr.
JWST-ERS-01373. TF and EL acknowledge support from  ANR  Grant  ANR-21-CE49-0020-01. LNF was supported by a  European Research Council   Consolidator Grant  (under the European Union's  Horizon 2020 research and innovation programme , grant agreement No  723890 ) at the  University of Leicester. RH was supported by Grant  PID2019-109467GB-I00  through  MCIN/ AEI/10. 13039/501100011033/ and by  Grupos Gobierno Vasco   IT1742-22. KdK acknowledges support from  NASA  through Grant  HST-GO-15425. 002-A  from the  Space Telescope Science Institute , which is operated by  AURA, Inc. , under  NASA  contract  NAS 5-26555. Some of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Data Availability Statement The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute, Observation #24 of ERS program #1373. The specific observations analyzed can be accessed with de Pater (2023) [Data set]. The data were reduced with the standard JWST pipeline, as described in Section 2. 1 (JWST pipeline, n. d. [Software]). The resulting fits files were read into IDL, and aligned by using the Fast Fourier transform (FFT) cross-correlation algorithm in IDL. To make spectra the authors used IDL routine apphot. pro (Perrin, 2013 [Software]), after expanding the images by a factor of 5, and interpolating to the smaller-sized pixels.
Formal structures are organizations characterized by high levels in terms of rules, procedures, coordination and control mechanisms. 1 They have been studied from the very origins of organizational theory  (Sine et al. , 2006). Typical examples of formal structures are well established organizations that have become more structured over the years. In the IT industry, examples are small or big organizations that edit and/or integrate software, like Windows, IBM or Smile. Knowing communities are informal networks where participants repeatedly interact, exchange and build knowledge  (Goglio-Primard et al. , 2020). They are "a voluntary association of actors, typically lacking an a priori common organizational affiliation (i. e. , not working for the same firm) but united by a shared goal -in this case, creating, adapting, adopting or disseminating innovations"  (West & Lakhani, 2008, p.
224). These communities can be compared with communities of practices  (Wenger & Wenger-Trayner, 2020)  and can evolve within, across or outside the boundaries of formal structures. We define open source communities as groups involving "software developers at many locations and organizations sharing code to develop and refine software programs"  (Lerner & Tirole, 2002, p. 197). They are an exemplary case of external knowing communities. Formal structures work more and more with external innovation communities  (Bogers et al. , 2019; Sarazin et al. , 2017). The main reason for interacting with such open communities is to contribute or/and benefit from the innovations developed in the community. Empirical studies highlight the importance of these external knowing communities in the process of creation and innovation within formal structures  (Cohendet et al.
, 2015). The research gap is based on three considerations. First, sharing innovation with communities is so far more studied from the point of view of the community than the one of formal structures  (Kim et al. , 2022). Besides, existing research on the interactions between formal structures and open source communities has qualitatively highlighted different forms of involvement for these formal structures in open source communities. This research has so far focused mainly on case studies. The different forms of categorizations proposed according to the cases studied do not allow to identify a unified structure of forms of involvement, quantitatively validated at the level of the industry itself. Second, researchers have identified a variety of categorizations that all combine aspects of use of open source software and aspects of community involvement, but differ in terms of the number of dimensions and characteristics.
Therefore, there is a need for research focusing on the interactions between a formal structure and knowing communities from the perspective of the formal structure. The open source movement is a good example of formal structures interacting with several external knowing communities. Open source appeared in the software industry in the early 1980s and is a new organization structure that involves "developers at many locations and organizations sharing code to develop and refine [software] programs"  (Lerner & Tirole, 2002, p. 197). At first a community-based software development movement  (West & Gallagher, 2006) , it has "reshaped the ways how software-intensive firms develop products and deliver value to customers"  (Linåker et al. , 2018). Well-known formal structures like Apple, Google, IBM or Microsoft use open source components and therefore cooperate through diverse manners with open source communities  (Bogers et al.
, 2004, pp. 596-599) , meaning that they must involve in open source communities on a regular and long-term basis. Ciesielska and Westenholz (2016, p. 347 ) have had a similar but more detailed approach, with six steps going from using open source software to becoming inter-connected members of open source software communities. Lastly,  Lisein et al. (2009, p. 23)  described one level of pure-sufficiency and three levels of community involvement. These authors suggest that the use of open source by the formal structure is a first step before being involved in knowing communities but they do not state it clearly as they do not conceptually distinguish them. In this research we separate the use of open source from the involvement in knowing communities, to propose measurement scales for each and to distinguish three forms of involvement, all of this at the theoretical and empirical levels.
Respondents were formal structures editing and deploying open source software. Both editors and service providers make similar license choices, have similar values and propose similar services. Therefore, it is better to study them as a whole  (Bonaccorsi et al. , 2006). The survey was performed online and sent to all members of the IT French professional associations in October 2017. 157 business managers, being specifically editors and/or service providers of open source offers, answered to the survey. Respondents were either executives or IT managers of the formal structures (Appendix A). We removed respondents that did not answer all the questionnaire, as well as outliers, either based on the absence of variance in their responses or on the Mahalanobis distance. After removing these missing data and outliers, we obtained a final sample of 126 open source editors and service providers.
Research question There is a lack of agreement on the most advanced categories of community involvement. Indeed, literature reveals that scales disagree on the ultimate levels. For instance, the "visible hand" highest level of Lisein is equivalent to the level 4 "companies leading open source software projects" in  Ciesielska and Westenholz (2016)  scale. Moreover, their level 6 "inter-connected members of open source software communities" is equivalent to level 2 "invisible hand inter-connected" in  Lisein et al. (2009)  scale. Facing this situation, we decided to perform a second study. The objective is to enhance our knowledge in the different categorizations and in particular to understand why in some cases the "inter-connected" involvement is considered as the ultimate level  (Ciesielska & Westenholz, 2016) , and why the "formalized" involvement is the ultimate level in other scales  (Lisein et al.
Qualitative data collection and analysis The qualitative study consists in case studies with three formal structures involved in professional open source associations, in open source knowing communities and respectful of the open source values (see Table  4 ). These formal structures are characterized by an interconnected involvement. We performed semi-directive interviews with their general managers and new product development managers, insisting on their interactions with open source knowing communities. In addition, we collected secondary data to learn about their offers, their activities and their community and professional association involvements. We also participated into professional meetings and read professional press about open source management to get a global view of the current challenges at field level.
We performed a content analysis using the open source qualitative thematic analysis RQDA 8  in R  (Chandra & Shang, 2017). To ensure data accuracy, we offered the transcript back to the interviewers for review  (Smith et al. , 2018). Altogether, interviews amount to 41964 words. We coded all answers following the dimensions identified for our scales (see Table  2 ) to understand how managers from inter-connected formal structures think and behave. We triangulated our interviews with information coming from business meetings with open source managers and from professional press, in order to check our construct validity.
The comprehensive measure for formal structure use of open source includes six items. For formal structure community involvement, we use 17 items. All our questions are 5-point Likert assertions. We performed an exploratory factor analysis (EFA) with R software to identify the dimensions of the scales and assess how much each item is useful to explain each dimension, then purified our scales for the use of open source and for community involvement. We estimated the optimal dimensions amount for our two scales, by using the eigenvalue test, the "scree" test and the minimum restitution test. It is advised to stop extracting when 60% of the explained variance has been extracted  (Hair et al. , 2006). We performed several rounds for each scale using Varimax rotations. We removed items, one after another, if they had a communality lower than 0.
Full inter-connection with communities appears to be a myth that can't really be achieved Concerning the use of open source tools and methods, the literature proposed that formal structures use open source as final users or get involved in communities. Our quantitative study 1 demonstrated two facts: First that the use is "large", that is not an everyday use of open source software like LibreOffice or Mozilla, but rather a use of methods and tools; Second, that the inter-connected involvement is characterized by membership and contribution to external communities. The qualitative study enabled us to better understand the two fundamental aspects of this involvement, membership and contribution. Concerning membership, formal structures explained that they are members of several communities, but not as many as the number of components they use.
Our case studies highlight that it comes from the software itself. Formal structures can't create a software entirely on their own. They need to rely on many software projects to be able to build their own offers. Therefore, formal open-source structures rely on many open-source knowing communities, slowing down interconnection. "In our products, there are 20. 000 or 30. 000 lines of code that we wrote. and there are between 100. 000 and one billion lines that we did not write, depending on the systems that we sell. (…) In a whole, there might be a ratio of 1 to 10 between what we created and what we integrated" (Firm 1, manager 1). "Having a community is an important factor in choosing software, almost more important than the product itself. For example. yesterday I had to choose between two Python libraries: I checked the number of developers for each project in Github, the pace of development, who participates (Firm 1, manager 1).