dataesr/scientific-paragraphs-categorization huggingface dataset:
Prior to this advance, water vapor could only be collected using tedious cold trapping methods (e. g. ,  Jacob and Sonntag, 1991; Steen-Larsen et al. , 2011; Angert et al. , 2008) , deployed until sufficient amounts were collected to allow subsequent transfer to vials and later mass spectrometer analyses. Such a task was almost impossible to perform routinely (e. g. ,  Schwarz et al. , 1998). The first implementation of continuous in situ isotopic monitoring of surface water vapor isotopic composition above an ice sheet was achieved at NEEM, NW Greenland, during summer field seasons  2010 -2012 (Steen-Larsen et al. , 2013 , 2014). In parallel, repeated sampling of surface snow was also implemented for laboratory isotopic analyses. The combined snow and vapor datasets revealed changes in the isotopic composition of surface snow in response to synoptic changes in the atmospheric water vapor isotope values and exhibited a strong diurnal variability in near-surface water vapor isotopic composition.
3. 5). The mean temperature for the month of January 2014 was -25 • C, similar to the climatological average (-25 ± 2 • C, 1998 to 2013). Both temperature values are based on hourly data from the permanent automatic weather station (AWS) at Kohnen (hereafter AWS9, described in Sect. 3. 1). During clear-sky conditions, the net radiation at the surface is predominantly positive during the day (i. e. , a dominant energy gain by shortwave radiation from the sun despite a high albedo of the snow surface) and negative during the night (i. e. , a net loss by longwave emissions from the surface), imprinting a strong diurnal cycle to the surface temperature, with a peak-to-peak amplitude of 14 • C  (Van As et al. , 2005). 3 Material and methods.
5. The corrected isotopic composition is δ * corr ± dδ * corr , where dδ * corr is the final uncertainty containing both the precision and the accuracy on the corrected measurements. We apply the three corrections (humidity isotope response, conversion to the VSMOW-SLAP scale, drift removal) to calculate the corrected isotopic composition at a given time: δ * corr = α * × δ * raw - * -µ *. We obtain a final uncertainty on the corrected isotopic composition by applying an error propagation calculation, assuming no correlation between the three corrections: δ * corr 2 = P 2 * + A 2 * , with P 2 * = α 2 * × dδ * raw 2 + (d * ) 2 + (dµ * ) 2 A 2 * = (dα * ) 2 × δ * raw - * 2. The parameter A * stands for the accuracy of the measurements and P * stands for the precision of the measurements. We have attributed the part A * to the accuracy because the uncertainty on the VSMOW correction will affect the mean value of the data over the campaign.
Atmospheric simulations ECHAM5-wiso  (Werner et al. , 2011)  is the isotopic version of the atmospheric general circulation model ECHAM5  (Roeckner et al. , 2003). Simulations from ECHAM5-wiso are implicitly nudged to the European Center for Medium- Range Weather Forecasts (ECMWF) ERA-interim reanalyses data  (Berrisford et al. , 2011)  using 6-hourly pressure, temperature, divergence and vorticity fields  (Rast et al. , 2013). Sea surface temperatures and sea ice coverage are derived from the ERA-interim dataset too. For our purpose this model has been run with a high vertical and horizontal T106L31 resolution (31 levels, 1. 1 • in longitude × 1. 1 • in latitude). The lowest model level (about 60 m above the surface) has been selected followed by a bilinear interpolation of nearby model grid points to the location of Kohnen base (75 • 00 S, 0 • 04 E).
There could theoretically be some inconsistencies between the winds and the SSTs from different reanalyses datasets, but the impact should be very small due to the overall consistency between the two reanalyses datasets and due to the strong nudging of the winds, preventing any drift. Because such high-resolution simulation is costly, the simulation has been started in January 2013 but inspection of simulated time series show that the spinup in sufficient. Three selected outputs from both models are calculated at a specific height, 10 m for the wind speed and wind direction, and at 2 m for temperature. However, the reader should notice that simulated parameters such as humidity or water vapor isotopes are extracted from the first vertical model level (which represents a height of 60 m above ground) whereas the in situ observations are measured close to the surface.
Observed and simulated day-to-day variability In order to estimate the magnitude of the day-to-day variability, days with data gaps larger than 8 h have been removed from the dataset (on 16, 28 and 29 December and 13, 14, 17, 18 and 21 January) and then 29 daily mean values of q, T , dexcess and δD have been calculated for the observations and the model outputs. Average and standard deviation of these parameters over the 29 mean values are detailed in Table  3. The signal from the temperature, humidity mixing ratio and water vapor isotopes measurements is stable, with a much lower variability on the day-to-day scale compared to the diurnal scale (Fig. 2 ). The mean humidity mixing ratio measured at 3 m over the study period is 1100 ppmv (Table  3 ), with a maximum of 2200 ppmv on 22 December at 15:00 UTC.
Air-snow exchanges In order to document the isotopic exchange between the surface snow and the overlying water vapor, snow surface samples were collected from three snow patches, with five jux- taposed replicas per patch and per hour (protocol described in Sect. 3. 5). In the isotopic composition of the snow surface presented in Fig. 4 , each hourly data point is the average of the five snow samples, with a standard deviation of 0. 03 ‰ for δ 18 O and 0. 2 ‰ for δD. We identify a clear diurnal cycle in the snow with a significant peak-to-peak amplitude of ∼ 3 ‰ for δD and ∼ 0. 4 ‰ for δ 18 O, in phase with the diurnal cycle in the air (Fig. 4 ). The mean deuterium value of the snow patches varies from -296 to -316 ‰, showing that the texture of the snow patch and its isotopic composition could be related (Table  6 ).
Conclusions Continuous measurements of temperature, humidity mixing ratio and water vapor isotopes were performed during summer 2013/2014 at Kohnen station in East Antarctica. These data highlight a strong diurnal cycle, in contrast to rather stable day-to-day mean levels over 1 month of observations. During our monitoring period, the surface vapor isotopic composition was therefore more driven by local processes than by synoptic changes. Outputs from the two AGCMs (ECHAM5-wiso and LMDZiso) show in general good agreements with the observations. However, the surface temperature variations simulated by the models have an amplitude 50 % lower than observed by  Van As et al. (2005) , likely due to the difficulty to simulate the longwave radiative budget (related to the cloud cover and snowfall events).
Acknowledgements. This first paper is dedicated to  Dominique Hirondel , former teacher of history of sciences, for being a role model for scientific curiosity and fascination with reality. The scientific campaign would not have been possible without the efficient work of the AWI logistic staff. Special thanks to  Carleen Reijmer  for providing the data of AWS9, to  Michael Schaefer ,  Gert Koenig-Langlo  and  Bernd Loose  for their contribution to the meteorological observations and to  Max Berkelhammer  for his valuable contribution to the final version of the manuscript. We also are grateful for the detailed and constructive comments offers by one anonymous reviewer and  Bradley Markle , which improved the paper significantly. LMDZiso simulations were performed on the Ada supercomputer at  IDRIS  under  GENCI  project  0292.
SETTING UP MITAP: COMMUNITY BUILDING AND INITIAL ANALYSIS Our goal was to provide a reporting framework that will make differences and similarities of tolAPC transparent. We therefore set out to create minimum reporting guidelines for the production process of tolAPC used in pre-clinical and/or clinical studies. We call this MITAP (Minimum Information about Tolerogenic Antigen-Presenting cells). A critical part of building the MITAP document was community building; to enable data sharing and commonality, the document must be broadly reflective of the opinions and current practices of many laboratories within the discipline. For this, a community is needed to provide information, feedback and tests subjects. Helpfully, we recently established a consortium of investigators working in the field of tolerogenic cellular therapies, with the aim of jointly addressing issues related to the translation and clinical application of these new treatments.
Secondly, we gathered bibliographic databases from all willing AFACTT participants; abstracts were gathered from PubMed, and analysed computationally to find words that were over-represented in these abstracts compared to the bulk of PubMed. Finally, we built some bespoke software to crowd-source community feedback on these terms using a simple web interface. The conception of MITAP took place in April 2014, at the first AFACTT meeting; the overall process of creating the MITAP document took 18 months (Fig. 1 ), which reflects the nature of the process i. e. , the active involvement of a diverse group of investigators and the absolute need for incorporating feedback and suggestions from this group.
OVERVIEW OF THE MITAP DOCUMENT The MITAP document is divided into 4 sections that encapsulate the most important parts of the tolAPC production process in a chronological manner. The sections describe the cells at the start of the production process, during the differentiation/induction of tolerogenicity process, and at the end of the production process, prior to injection into the recipient or use in experimental assays. A final section is dedicated to aspects that are important for understanding the application and purpose of tolAPC. The sections are described in more detail below; for all the sections and subsections the document states clearly whether the investigator must (required), should (if available) or may (optional) provide the relevant information. It also advises on the use of existing taxonomies and databases to provide the information in a uniform manner, and it suggests the use of other MIMs where appropriate.
TESTING MITAP: THE "20 QUESTIONS" EXPERIMENT The MITAP document was developed collaboratively by scientists in the AFACTT consortium and should, therefore, be usable by this community. However, we wished to test directly whether the document as it stood was comprehensible by individuals from the discipline, and that data could be entered in a consistent manner. We therefore performed a ''question-and-answer'' experiment, a variation on the spoken parlour game ''20 questions,'' popular in the 19th and 20th centuries. We collected a list of terms-that is short descriptive phrases that might be found in a description of a procedure to generate tolAPC, each of which would fit into one of the categories defined by the MITAP document. We then asked a number of scientists from the discipline, our test subjects, to place these terms into the relevant category from MITAP, which we call the test task.
Third, our test subjects should also be representative of the discipline. Therefore, we asked those who contributed terms to also nominate a test subject. Finally, the test subjects were people who had not directly contributed to MITAP, nor seen the document, nor were involved in contributing terms. For the test itself, test subjects were presented with a set of terms and for each asked to assess into which category it should be placed. Test subjects were given no guidance on how to categorise terms except for the MITAP document. In fact, they were given a slightly reduced MITAP document; the non-normative (i. e. , explanatory and not a strict part of the standard) examples were removed for the purpose of this experiment. Subjects were also asked for some basic metadata (level of experience, discipline and so forth).
PREVALENCE OF MITAP DATA IN EXTANT PAPERS The purpose of the MITAP document is to ensure that authors provide sufficient basic information about their tolAPC production protocol. An implicit assumption is that currently some or all of this information is not being routinely described. To test this assumption, we reviewed a number of papers about tolAPC, and for each, we determined whether the data required by the MITAP document was present or not. In detail, forty-two tolAPC papers were selected (predominantly from members of AFACTT or from researchers wellknown in the field), randomized and the first twenty were read in detail. For each section of MITAP, we determined whether the information required was either: directly stated in the paper or reference (Fig. 3 : green squares), whether it was possible to infer from other information given in the paper (Fig.
org). Both of these organisations have sustainability as their key aim. Maintenance of MITAP documents as live, web-resources therefore requires no active contribution from the authors of this paper. Either archive. org or w3id. org need to fail before MITAP cannot be maintained at the stated location. We have also given explicit consideration to data formats for our documents. Resources are available as Word docs and PDFs, providing typographical elegance and familiarity to the community, but also an extremely simple HTML representation, ensuring vendorneutrality and future-proofing of the knowledge contained within them.
DISCUSSION The tolAPC community has advanced significantly in the last 15 years, from the initial discoveries of how to culture APC with potent immune-regulatory properties, through to clinical trials. At the current time, however, most investigators are operating as a ''cottage industry'': individual labs working with different cell isolation techniques, procedures to induce stable tolerogenic properties in the cells, and investigating different therapeutic outcomes. Against this heterogeneous background, it becomes difficult to compare investigations between different laboratories, raising concerns about reproducibility  (Ioannidis, 2005; Boulton, 2016). The MITAP document that we have described here will, of course, not address all of these issues, however as a minimum guidelines tool for reporting of protocols, it should at least address the issue of data heterogeneity; different laboratories may still use different protocols to generate tolAPC, but at least we will be clear about this diversity, and have a greater understanding of what these differences are.
Furthermore, unlike many minimum information efforts, we have considered upfront long-term sustainability of the documents and the URIs used to refer to them. Our sustainability plan is robust and does not require active involvement of any of the authors. Of course, there is much work remaining. The production of tolAPC is only a small part of the overall process. Moreover, we are only attempting to standardize the reporting of protocols, and not the standardisation of the protocols themselves. Finally, we currently have no common location or database which can store the experimental data, such as exists for instance with microarray or genomic data. MITAP is a first step, laying the groundwork for further collaboration.
We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B.
This reinforces the importance of understanding the regulatory mechanisms that allow the B. subtilis bacterium to survive and adapt to such conditions. As a model organism, literature for B. subtilis regulation is extensive and several resources/databases are available. A regulatory network model for the central carbon metabolism was made available by  Goelzer et al. (2008). Multiple inferred networks based on expression data have also been proposed in the literature  (de Hoon et al. , 2003; Steggles et al. , 2007; Fadda et al. , 2009). RegPrecise  (Novichkov et al. , 2010a) , a resource for transcription factor binding site (TFBS) based network inference also provides a network for B. subtilis  (Leyn et al. , 2013). Subtiwiki  (Florez et al. , 2009; Michna et al. , 2014 ) is a community collaborative resource for B.
subtilis that includes a vast compendium of regulatory information. DBTBS  (Sierro et al. , 2008)  is another B. subtilis comprehensive resource of regulatory data with promoters, TFs, TFBS, motifs and regulated operons. Our novel genome-scale reconstruction of the B. subtilis regulatory network integrates the previous work from the  Goelzer et al. (2008)  literature and the other notable resources for regulation described above  (Sierro et al. , 2008; Florez et al. , 2009; Novichkov et al. , 2010a; Mader et al. , 2012). We reconciled our new model against a large set of highquality gene expression data  (Buescher et al. , 2012; Nicolas et al. , 2012). For the process of reconciliation with expression data, we introduce the concept of Atomic Regulons (ARs). ARs are sets of co-regulated genes that share the same "ON" and "OFF" expression profile (meaning the genes in these sets are "ON" and "OFF" in the same conditions) (submitted).
MATERIALS AND METHODS In this work, we explore the reconstruction of regulatory networks using two different approaches. In a first approach, we combine the information available in databases with notable regulatory transcriptional data for B. subtilis into a comprehensive manually curated regulatory network. In the second approach, we developed a methodology, dubbed "AR inference", to infer regulatory interactions from a combination of gene expression data, predicted operons, and SEED subsystems-based annotations. For this purpose, we chose a dataset of expression data comprised of 269 samples across 104 different experimental conditions  (Buescher et al. , 2012; Nicolas et al. , 2012). We selected this dataset for its remarkable quality, consistency, and coverage. All work to produce this dataset was conducted according to a pre-agreed Standard Operating Procedure, and only 4.
4% of the known CDS in the selected strain were not expressed in any tested conditions. The whole data set is deposited in the NCBI Gene Expression Omnibus (GEO) under the accession number GSE27219. All of the different experimental conditions were performed using the BaSysBio 1  reference strain BSB1. This strain is a tryptophan-prototrophic (trp+) derivative of the B. subtilis 168 trpC2 strain  (Anagnostopoulos and Spizizen, 1961). Finally, to take advantage of both approaches and expand our knowledge of the B. subtilis regulatory network, we propose a process to reconcile their output. We define an AR as a set of genes with identical binary (ON/OFF) expression profiles. That is, all the genes included in the same AR will always have the same state: either "all ON" or "all OFF". This notion has meaning only in a simplified model of the cell in which genes are either ON or OFF in any condition.
The Web Resource We developed a set of web tools to visualize all ARs we constructed for B. subtilis, along with the associated expression profiles and experimental metadata. The web tools are available at http://tinyurl. com/AtomicRegulons. The initial page provides the user with a list of the ARs, some of which have general descriptions. The website can be used for other analyses of the expression data. That is, AR data presented on the web site for many different species can be used for studies outside the scope the work presented in this manuscript.
Draft Regulatory Network of Bacillus subtilis from Manual Curation We introduce a manually constructed and curated model that describes the current state of knowledge of the transcriptional network of B. subtilis. The model corresponds to an updated and enlarged version of the regulation network in the central metabolism originally proposed in  Goelzer et al. (2008). We have firstly extended that original network to the whole genome by including the information from the DBTBS database  (Sierro et al. , 2008). The DBTBS compendium of regulatory data includes promoters, TFs, TFBS, motifs, and regulated operons. The addition of the DBTBS led to a significant increase in the size of the regulatory network (Table  1 ). Additionally, we consolidated our network with all the information on regulation included in SporeWeb  (Eijlander et al.
, 2014)  and in Subtiwiki  (Florez et al. , 2009; Mader et al. , 2012)  as of March 2013. Subtiwiki is the reference community-curated resource for B. subtilis that was integrated into the 2012 release of DBTBS. This consolidation with Subtiwiki resulted in some revision of regulatory data included in the original network by  Goelzer et al. (2008). Also, it significantly enlarged the network with respect to other microbial processes. All the above data reflect experimentally validated regulatory interactions. Additionally, RegPrecise  (Novichkov et al. , 2010a) , a database that provides tools  (Novichkov et al. , 2010b)  for prediction and curation of regulons, recently released their reconstruction of the regulatory network for B. subtilis  (Leyn et al. , 2013). Reconciliation with the RegPrecise inferred network resulted in the addition of a total of 39 regulators and their target gene sets to our experimentally validated network.
Atomic Regulons for Bacillus subtilis With the initial reconstruction of our new regulatory model of B. subtilis complete, we next applied gene expression data to reconcile and validate our model. This process began with a survey of the data present in current expression databases. As of May 2015, there were ∼1750 datasets in GEO  (Edgar et al. , 2002)  related to B. subtilis strains. However, the utilization of all of these data in our model validation posed a challenge, as protocols vary from lab to lab, and various expression platforms can produce results that are difficult to merge. Thus, we selected a single dataset, comprised of 269 samples collected from 104 different growth conditions  (Buescher et al. , 2012; Nicolas et al. , 2012). We used this data with our new algorithm to infer a set of ARs for the B.
The capacity of our AR inference methodology to divide these genes into separate ARs that conform perfectly with their distinctive stimuli demonstrates the robustness of the approach. Two of these ARs, #254 and #625, respond to uncharacterized effectors in our model (Figure  4A ), and these ARs are active in no more than five and three experiments in our expression dataset respectively (Figure  4B ). For AR 625 we checked the experiments in which the genes were ON (Table  3 ). Genes in AR 625 were found to be "ON" in experiments that tested gene expression at regular intervals after sporulation was induced. A detailed description of the study can be found by looking at the associated study number (in this case "study0003" 2  ). Sporulation was induced with the use of CH medium  (Sterlini and Mandelstam, 1969) , and cells were harvested at hourly intervals, with the genes in our ARs being ON in the late intervals of the study.