dataesr/scientific-paragraphs-categorization huggingface dataset:
Control-FREEC transgene quantification Copy number quantification was performed using Control-FREEC version 11. 5  (36). The signal from the ectopic CS38-40 (test dataset) was computed based on the TgN  (38) (39) (40)  total input gDNA data of the ChIPmentation experiment (see ChIPmentation below). Next, the signal from the endogenous region CS38-40 (control dataset) was created by pooling total input gDNA data of four samples that were all TgN(38-40)-negative and wild-type for region CS38-40 in chromosome 2, each processed independently. For both test and control datasets, the Control-FREEC signal, expressed as the number of reads scored for non-overlapping genomic windows of given sizes  (49) , was calculated along a 7 Mb region of chromosome 2 including the HoxD locus (chr2:71000000-78000000).
The above analysis was carried out using two different window sizes: 1 and 2 kb (see S2B Fig). Then, the software calculated the test/control signal ratio, that is the number of reads from test divided by the number of reads from control, for each window, multiplied by two in order to obtain absolute allele counts. At last, the Control-FREEC software evaluated copy numbers along the 7 Mb chr2 region starting from the (test/control)•2 ratio by a maximum (log-)likelihood estimation  (36).
5 TgN  (38) (39) (40) /TgN  (38) (39) (40) ; del(CS38-40) -/-headless and tailless embryo. The sample was proteinase K digested in digestion buffer (50 mM Tris-HCl pH 8, 10 mM EDTA pH 8, 200 mM NaCl, 0. 5% SDS) at 55°C while shaking for 48 hours. HMW gDNA was purified with two successive rounds of phenol-chloroform extraction, followed by ethanol precipitation. Size selection was carried out using 0. 8x SPRI magnetic beads. nCATS was performed in two independent Cas9-mediated release reactions corresponding to the two different pools of sgRNAs and the products from both reactions were pooled together prior to sequencing on MIN106D flow cell. MinION output data in fast5 format were converted to fastq using the Guppy basecaller (version 3. 1. 5) (Oxford Nanopore Technologies) and the reads were mapped with minimap2 (version 2.
15) onto mm10 and the TLA-derived configuration that assumed a 63. 2 kb integration, comprising one entire copy of region CS38-40 followed by the partial CS38 segment, after position chr10:97019221 (mm10) (see Fig  2D, top). Reads mapping on the integration site (mm10, chr10:97018026-97020425) or transgene region (chr2:75122684-75160161 of mm10) sequence components of the TgN  (38) (39) (40)  mutant construction were converted from fastq to fasta in order to produce the dot plots displayed in Fig 2E. This was achieved using a Perl script as in  (50) , with the following modification. 20 bp of the MinION reads were tested against the reference for 5 bp-sliding windows and only 20-mers completely identical to unique 20-mers in the reference were kept. The output was then processed in R (www.
ChIPmentation data analysis was performed as described in  (52) , mapping the reads on either the mm10 reference genome or TgN  (38) (39) (40)  custom genome, with the following adaptation. All reads were kept for the TgN  (38) (39) (40)  mapping and hence those aligning to the duplicated sequences were not discarded. These reads were randomly attributed to one or the other location. Peak calling of CTCF and RAD21 was achieved using the MACS2 algorithm (Galaxy Version 2. 1. 1. 20160309. 3 with default parameters)  (53, 54)  on each replicate and the union of the peaks obtained in each replicate was used in the figures. CTCF site orientation was determined using CTCFBSDB 2. 0 (http://insulatordb. uthsc. edu/) (55) with MIT_LM7 motif position weight matrix.
Hi-C Hi-C was performed as in (  5 ) and  (58). For each genotype (i. e. wild-type or TgN  (38) (39) (40) /TgN  (38) (39) (40) ; del(CS38-40) -/-), one sample corresponding to four E12. 5 whole limbs was used as starting material. Hi-C libraries were multiplexed and sequenced so as to obtain 75/75 bp paired-end reads with an Illumina NextSeq (first run, 80 million reads per sample) or HiSeq 4000 (second run, idem). Hi-C data analysis was as in  (58) , with some modifications. HiCUP (version 0. 7. 3) (59) was applied providing either the mm10 reference genome, or the custom genome of TgN  (38) (39) (40). For the TgN  (38) (39) (40)  mapping, the pipeline was adapted in order to prevent removal of reads aligning to the duplicated sequences of this line. All valid hybrid pairs were kept since no MAPQ filter was applied.
Sequencing data analysis and display Chromatin immunoprecipitation (ChIP-seq and ChIPmentation) data were analyzed on our Galaxy platform  (65). Chromosome conformation capture (4C-seq and Hi-C), MinION and TLA data, as well as total input DNA data for Control-FREEC were analyzed through the Scientific IT and Application Support Center of the Ecole Polytechnique Fédérale de Lausanne (EPFL). Data were plotted using the pyGenomeTracks visualization tool (https://github. com/deeptools/pyGenomeTracks)  (61, 66). Gene annotations shown in Figs. (38) (39) (40)  transgene or surrounding regions: upstream of the transgene in chr 10 (up); one quarter (1/4), halfway (1/2) or three quarters (3/4) into the transgene; 3' part of transgene vector (pEpi3) and downstream of the transgene in chr10 (down). _Two pairs of primers (1 or 2) were design to target the same region.
ACKNOWLEDGEMENTS We would like to thank  Thi Hanh Nguyen Huynh ,  Sandra Gitto  and  Bénédicte Mascrez  for help with mice breeding and genotyping;  Mylène Docquier ,  Brice Petit ,  Didier Chollet  and  Christelle Barraclough  from the  Geneva Genomics Platform , as well as  Bastien Mangeat ,  Elisa Cora  and  Lionel Sylvain Ponsonnet  from the  EPFL Gene Expression Core Facility. We are grateful to all members of the  Duboule laboratories  for comments and discussions. This work was supported by funds from the  European Research Council  (grants SystemHox No. 232790  and RegulHox No. 588029 ), the  Swiss National Science Foundation  (No. 310030B_138662 ), the  University of Geneva  and the  EPFL  (to D. D. ). A. W. was supported by an  Excellence Master Fellowship  of the  Faculty of Science (University of Geneva).
Data availability All datasets produced in this study were deposited in the Gene Expression Omnibus (GEO) under the accession number GSE166584. ChIP-seq data of CTCF and RAD21 in wildtype E12. 5 whole limbs were retrieved from the GEO accessions GSM2713709 and GSM2713714, respectively  (22). H3K27ac ChIP-seq data in wild-type E12. 5 distal forelimbs were retrieved from GSM2713703  (22).
Alteration of local chromatin structure upon integration of the TgN(38-40) construct. We next investigated the conformational state of the region hosting the TgN(38-40) construct in chromosome 10 using our control Hi-C dataset and observed the presence of a 1. 2 Mb-large, well defined TAD wherein the transgene had integrated (Fig 3B). This domain contains relatively few CTCF sites (~5 sites/Mb) as well as a single gene, Btg1 (Fig 3, bottom). Examination of published ChIP-seq datasets for the active histone mark H3K27 acetylation (H3K27ac) in E12. 5 distal limb cells (22) revealed only two acetylated regions within the TAD, corresponding to either the promoter of the Btg1 gene, or a region at the telomeric (right) TAD border (Fig 3C).
Study sites The studied sites are including the entire French range of A. tragacantha, which is situated in Provence on the Mediterranean shoreline from 5. 29°E; 43. 29°N to 5. 88°E; 43. 04°N (Fig. 1 ). More precisely, the SILENE database  (Delauge et al. , 2013)  records the occurrences of A. tragacantha in the Bouches-du-Rhône and Var counties in the vicinity or inside five cities: Marseille, la Ciotat, Saint-Cyr, Six-Fours-les-plages and La Seyne-sur-mer (Fig. 1 ). Near Marseille, the western occurrences, from 5. 29°E to 5. 40°E longitudes, are in an area now included in the Calanques National Park, created in April 2012. These sites occur on compact limestone in continental or insular shoreline. The other populations occur from 5. 60°E to 5. 70°E longitudes, on different substrates: dolomitic (Cap Canaille near the city of La Ciotat), siliceous conglomerates (Bec de l'Aigle close to the city of La Ciotat), or sandy soils (city of Saint-Cyr).
, 2015; Robert-Peillard et al. , 2015). Five size stages were defined based on the mean diameter classes: inferior to 10 cm; between 10 and 50 cm; between 51 and 100 cm; between 101 and 200 cm; and superior to 200 cm. Four classes of foliar necrosis were considered: inferior to 6 / 27 10%; between 10 and 50%; more than 50%; and dead individual (i. e, 100%). A grid of 100 m² squares  (QGIS Development Team, 2020)  was used to calculate the Area Of Occupancy (AOO) of the species. Within each square of the grid, spatial requests in QGIS software  (QGIS Development Team, 2020)  were performed to count the total number of individuals, then the number of individuals within each of the size stages and necrosis classes. Each square was used as a unit sample to summarize demographic data and assess densities within the A.
Niche analyses To provide a comparative framework in niche analysis, we searched for plant species significantly associated to A. tragacantha (hereafter called "companion species"). To define this subset of companion species, we defined two groups of relevés according to the presence and absence of A. tragacantha and used the "multipatt" function of the indicspecies packages in R software which is based on specificity and fidelity criteria  (De Caceres and Legendre, 2009). A strong value close to 1 indicates a strong association between A. tragacantha and the focal species (e. g. it is present and abundant only when A. tragacantha is present). To design a manageable subset for analyses, we selected the plant species having an indicator value superior or equal to 0. 5.
Outlying Mean Index multivariate method (OMI) Our first approach was based on a multivariate ordination method that uses environmental and species assemblage data to correlate the occurrence of many species with ecological factors. Dolédec et al. (2000)  demonstrated that OMI is suited for finding the most structuring environmental gradients in species distributions. The ordination diagrams of OMI analysis from the niche() function of the ade4 package in R software  (Dray and Dufour, 2007)  are based on the table of normalized environmental variables and the floristic table. In the OMI scatterplot, the distance of the centroid of a species to the origin of the diagram is an estimate of the environmental marginality of this species. A species with a high marginality is constrained by a narrower environmental requirement compared to other species, i.
tragacantha to ecological gradients. Scripts and data of our analyses are deposed in DRYAD database (doi:10. 5061/dryad. bzkh1898p). We used the following packages: ade4  (Dray and Dufour, 2007) , adegenet  (Jombart, 2008) , labdsv  (Roberts, 2013) , gjam  (Clark et al. , 2017) , and ggcorrplot  (Kassambara, 2016). ). Overall, the mean proportion of individuals inferior to 10 cm in size is 4. 3% but more than half of subsectors (51. 5%) have no small individuals. The highest numbers of small individuals are observed in the "Calanques littoral" sector (especially "Escalette", "Goudes", and "Cap Redon" subsectors) where the proportion vary from 11 to 16%. The foliar necrosis is high, most individuals (66. 3%) present a foliar necrosis superior to 10% (Table  2 , Fig. 2 ) and 22% have a foliar necrosis superior to 50% of the cushion.
Acknowledgements We thank the  European Union  for financial support of the  LIFE Habitats Calanques  project ( LIFE16 NAT / FR / 000593 ,  2017-2022 ); the  Regional Agency for the Environment -Regional Agency for the Biodiversity (ARPE-ARB)   Provence-Alpes-Côte-d'Azur for the coordination of the LIFE Habitats Calanques  project; the  Departmental Council of Bouches-du-Rhône , the  City of Marseille  and the  Calanques National Park  for field access,  Daniel Pavon  ( IMBE ) for its expertise on floristic taxonomy.
The covariates were standardized to allow for comparison, their means and SDs are given in Table  S1. We used weakly informative priors (e. g. , b i N ð0; 1Þ) for regression coefficients, uniformpriors Uð0; 1Þ for live survival probabilities, and Uð0; 0:5Þ for detection probabilities and η that were known from preliminary trials to be much smaller. A second set of more informative priors (Beta priors) was considered in additional analyses (see code folder in  Barraquand & Nielsen, 2021)  for robustness. The empirical support for the models was evaluated using Bayes factors, using the R package bridgesampling  (Gronau, Singmann & Wagenmakers, 2017). We also used PSIS-LOO  (Vehtari, Gelman & Gabry, 2017)  to compare model predictive abilities. For a guide to Bayesian model comparison, see  Hooten & Hobbs (2015).
Models have been fitted in Stan  (Carpenter et al. , 2017)  within R (R Core Team, 2017; Stan Development Team & others, 2018) using the Forward algorithm for Hidden Markov Models  (McClintock et al. , 2020) , which helps to increase speed  (Yackulic et al. , 2020)  and convergence. Similar estimates have been found computing the full latent states of the model (i. e. , probability densities for the state of each individual at each moment in time) using JAGS for the simplest model without covariates, but these converged poorly. We ran all models for 1,000 iterations after a warm-up of 1,000 iterations and 3 chains, convergence was considered fine as 0:999 < R < 1:001. Code and data can be found at https://github. com/fbarraquand/Gyrfalcon_CMR and  Barraquand & Nielsen (2021). For models B and C, data on gyrfalcon capture histories were truncated, excluding data before 1981 (i.
ACKNOWLEDGEMENTS We thank the  Bird Ringing Office of the Icelandic Institute of Natural History  for access to the gyrfalcon ringing data. Since 1994 the gyrfalcon studies have been part of ÓKN's job obligations at the  Icelandic Institute of Natural History. ÓKN thanks all the people that have worked with him in the field over the years and helped with gyrfalcon ringing. FB thanks  Hiroki Itò  and  Bob Carpenter  who translated  Marc Kéry  and  Michael Schaub 's code into Stan, giving us a template to transition our JAGS code much more easily to Stan. We thank both referees for constructive criticism that prompted us to compare models more systematically.
Data Availability The following information was supplied regarding data availability: All data and code is available at GitHub and Zenodo: -https://github. com/fbarraquand/Gyrfalcon_CMR -Frédéric Barraquand & Nielsen Ólafur Karl. (2021). Gyrfalcon survival estimation. Zenodo. https://doi. org/10. 5281/zenodo. 5459150.
Ocean temperature observations are crucial for a host of climate research and forecasting activities, such as climate monitoring, ocean reanalysis and state estimation, seasonal-to-decadal forecasts, and ocean forecasting. For all of these applications, it is crucial to understand the uncertainty attached to each of the observations, accounting for changes in instrument technology and observing practices over time. Here, we describe the rationale behind the uncertainty specification provided for all in situ ocean temperature observations in the International Quality-controlled Ocean Database (IQuOD) v0. 1, a value-added data product served alongside the World Ocean Database (WOD). We collected information from manufacturer specifications and other publications, providing the end user with uncertainty estimates based mainly on instrument type, along with extant auxiliary information such as calibration and collection method.
, 2015; Storto et al. , 2019) , providing initial conditions for seasonal-to-decadal prediction systems  (Doblas-Reyes et al. , 2013; Meehl et al. , 2014) , and climate model evaluation and development  (Eyring et al. , 2019). The International Quality-controlled Ocean Database (IQuOD) initiative represents a community effort to create the most globally complete temperature profile dataset, with comprehensive metadata and uncertainty information to promote progress in all the above research avenues. The IQuOD project supports the overarching goal of the Ocean Best Practices System: To have agreed and broadly adopted methods across ocean research, operations and applications (https://www. oceanbestpractices. org/), by creating a uniformly quality controlled data set with internationally agreed upon processes and expert quality control.
The freely available IQuOD v0. 1 (The IQuOD Team, 2018) is a value-added data product served alongside the World Ocean Database (WOD)  (Boyer et al. , 2018a)  that includes observational uncertainty estimates and important additional metadata and bias corrections for eXpendable BathyThermograph (XBT) instruments. IQuOD will facilitate improved ocean state estimate products through improved data quality information and bias correction, and the addition of uncertainty estimates. With uniform, community-wide quality control, the IQuOD could, for example, provide a uniform baseline for calculations across objective analysis products (e. g. ,  Cheng et al. , 2021). Furthermore, when such observational uncertainty information is fed into objective analysis methods (such as in  Mariano and Brown, 1992) , it will have the potential to lead to improved estimation of the uncertainty in gridded products and/or rapid model initialization as in  Simoncelli et al.
The WOD does not contain uncertainty information on individual measurements. A dataset integrating data from many sources (such as the WOD) is the simplest data product, from which many other products can be derived, and the uncertainty associated with the measurement is a starting point for uncertainty propagation, such as described in  Atkinson (2020). IQuOD v0. 1 uncertainties are estimates of the measurement uncertainty. Creators of data assimilation and reanalysis products have developed various tools to estimate propagation of uncertainties from individual measurements through to the final derived products. These estimates include not only the uncertainty associated with the measurement, but account for the propagation of errors and associated uncertainties to the end product  (Merchant et al.
, 2017). Tools developed to derive these propagated uncertainties include validation against withheld and independent datasets, using simple statistical techniques such as bias, root mean square error etc, or using methods such as objective analysis  (Good et al. , 2013; Storto et al. , 2019). The benefits of including uncertainty estimates on every observation in space and time is clearly demonstrated by  Merchant et al. (2017) , in their Figure  1. Some efforts have been made to assign uncertainties and apply uniform quality control to other climate datasets, e. g. , sea level from satellite data  (Legeais et al. , 2018) ; sea surface temperature  (Kennedy et al. , 2019) ; surface temperature  (Morice et al. , 2021; Osborn et al. , 2021) ; marine air temperature  (Cornes et al. , 2020) ; humidity  (Willett et al.
, 2020). Atkinson et al. (2014)  constructed a database of ocean surface and subsurface observations (known as HadIOD. 1. 0. 0. 0), sourcing "accuracy" values from the literature and representing these numbers as random measurement uncertainty in their paper. Historical uncertainty estimates are rarely available, recovery of this information requires extensive research into cruise reports and institutional records, many of which have not been digitized or are otherwise not easily searchable. For more recent sensors, manufacturers usually provide a nominal sensor uncertainty. Over time, instrumentation has changed and become generally more accurate and better calibrated, with narrower uncertainty. However, the WOD lacks sufficient metadata to enable accurate and consistent assignment of uncertainties.
In accordance with the GUM, IQuOD aims to keep bias corrections and uncertainties separate. Known biases in XBTs are corrected in IQuOD v0. 1, and a correction for biases in Mechanical BathyThermographs (MBTs) can be applied to IQuOD in the WOD select tool. All uncertainties in IQuOD v0. 1 are considered to be independent of systematic errors or biases and only contain uncertainty after corrections are made (even if biases have not been corrected). Accurate assignment of uncertainties is wholly dependent on the metadata associated with each profile in the WOD, and the lack of metadata and incorrect metadata in some profiles will result in incorrect assignment of uncertainties to these profiles. The uncertainty associated with these missing metadata has not been included in the overall uncertainties.
CHANGES IN INSTRUMENTATION OVER TIME Data collection methods for ocean temperature observations have changed over the course of time, with instrumentation becoming more sophisticated and generally more accurate. A nice overview can be found in  Abraham et al. (2013)  and  Gould et al. (2013) , and an in-depth review of early temperature data (1749-1868) in  Prestwich (1875). Early measurements were performed with an insulated wooden bucket, measuring the sample temperature with a mercury thermometer after the sample was collected and brought to the surface. Early ocean expeditions used maximum-minimum thermometers, which can only record temperature extrema in the water column  (Negretti and Zambra, 1874). The reversing thermometer was introduced in 1874 by  Negretti and Zambra (1874) , these thermometers required careful reading and recording of the information by the observer.
UNCERTAINTY ESTIMATES APPLIED TO TEMPERATURE INSTRUMENT TYPES IN IQUOD V0. 1 IQuOD v0. 1 contains Type B uncertainty estimates for temperature and depth, obtained from various sources including manufacturer specifications, journal reports and historical publications (Table  1 ). A single value for every temperature (and where available, depth) value in the IQuOD database was determined from the information gathered. For IQuOD v0. 1, these values are categorized by instrument type, and in some cases, year of collection and country responsible (Table  2 ). The depth uncertainties are similarly assigned, but in some cases vary with depth. The scheme used for uncertainty assignment is currently simple, but allows for flexible development in future releases. The HadIOD. 1. 0. 0. 0 database of ocean surface and subsurface observations  (Atkinson et al.
Errors in sample depth estimates could also result in systematic temperature errors. It is important to retrieve as much accurate metadata as possible for historical Nansen data to assist with understanding of errors and uncertainties associated with the depths of these temperature data. An uncertainty of 5% has been applied to bottle and reversing thermometer depth data following  (Brennecke, 1921)  who indicates errors in depth in excess of 5% were common in the era prior to reversing thermometer pairs. This uncertainty could be refined in future IQuOD releases where metadata become available on the specific instrument types or cruises.
MBT (Mechanical BathyThermograph) Mechanical BathyThermographs were developed during the 1930s and temperature profiles were collected from these instruments until the early 2000s  (Couper and LaFond, 1969; Boyer et al. , 2018b). Several modified MBT devices were developed, including the standard BT for different depth ranges, the sediment BT, the Sea Sampler and the Submarine BT  (Couper and LaFond, 1969). The MBT instrument can be deployed from moving vessels and data is recorded on a smoked glass plate which is then read using a calibrated viewer  (Couper and LaFond, 1969). These instruments are known to be subject to bias errors in both temperature and pressure  (Levitus et al. , 2009; Gouretski and Cheng, 2020). Corrections for biases in MBT data can be applied to IQuOD v0. 1 via the WOD Select tool.