dataesr/scientific-paragraphs-categorization huggingface dataset:
Case 1 2. 2. 1. Case 1 -motivation and design The Case 1 data set solely consists of an image representing an artificial color record of a core and no numerical data series was provided. The design of this exercise allows for a comparison of scenarios that are more naturally prone to cyclostratigraphic methodologies that are based on approaches that are purely visual, purely numerical or a combination of both. The eccentricity amplitude modulated precession cycles and obliquity-precession interference patterns in the provided image provide strong hints of the presence of an astronomical imprint and allow for the construction of an astrochronology, despite the limited amount of other stratigraphic constraints (see Section 2. 2. 2). To generate the image, the 21 July insolation curve at 55°N between 6 and 8 Ma was used  (Laskar et al.
It uses a specified feedback time and nonlinearity to model rapid warming and slower cooling. Originally, it was used to model relative ice volume and it is commonly driven by northern hemisphere insolation. Here, the insolation was mimicked by (Eq. (  1 )): Eq. (  1 ) is based on the suggestion that p-0. 5 t (standardized precession minus 0. 5 times standardized tilt/obliquity) resembles northern hemisphere insolation, but avoids suggesting a specific forcing latitude  (Lourens et al. , 1996). This nonlinear dataset was used from 1500-1200 ka and 1000-500 ka, omitting the time from 1200-1000 ka. In a next step the dataset was standardized and combined to reflect a depth series from 0 to 802 (500-1000, 1200-1500) depth units. Subsequently, an up-ramping artificial sedimentation rate from 1 to 1.
5 depth units per cm was applied. The resulting dataset was linearly interpolated using (time) steps of 1, resulting in 1002 data points. Finally, 0. 5 times standardized auto-regressive noise was added. The change in (artificial) sedimentation rate ranged from 1-1. 5 cm kyr -1 , which was deliberately lower than a factor of 2, allowing for the separation of precession-and obliquity components in the power spectrum of the signal in its entirety. The hiatus comprised half of a 405-kyr orbital eccentricity cycle, potentially allowing for its identification through a "too thin" expression of a 405 kyr cycle in the spatial domain. An R script in the Supplementary materials documents the creation of this signal.
Case 1 -results and observations Twenty responses were received that provided analysis of Case 1. The majority (55%) of submitted results for Case 1 reported a total duration estimate that differs less than 1% from the true duration (Fig. 5A ). Seven responses were within a 10% error. One duration Fig. 4. Case 3 illustration presenting the signal and questions. Additional stratigraphic information was provided in the accompanying text as well as a data file with the raw data. estimate was 25% too long and one 25% too short. The participants also reported an uncertainty estimate on their duration estimate. When the reported uncertainty was larger than the offset between the reported duration estimate and the true duration, the result was categorized as "conservative" (green background on Fig.
Cyclostratigraphy as a trainable skill The accuracy of submitted results does not seem to improve with increasing level of general experience in the field of cyclostratigraphy. In first instance, this observation may seem problematic, since intuitively one expects experienced researchers to do better, and emphasizing that someone can be trained to become more skilled at interpreting cyclostratigraphic data. Equally, however, during in-depth discussions at the CIP meeting, it became clear that experience must be seen in a broader context than just total years (as asked in the questionnaire: "How many years of experience do you have with cyclostratigraphy?"). The familiarity with certain types of sedimentary records, datasets and specific stratigraphic time intervals represents the key to more accurate results.
A clear initial distinction exists between records which can be tuned to a robust astronomical solution (Cases 1 and 2) and those for which no correlation template is available (Case 3). Discussions during the CIP meeting clearly highlighted that people with experience in astronomical tuning to astronomical solutions did better in Case 1 and 2, whereas participants with more experience in the astronomical calibration of floating chronologies for which no astronomical solutions are available performed better in Case 3. This is a crucial observation demonstrating that the quality of cyclostratigraphic analyses depends to some extent on past experience with similar datasets, and that one can improve their results by acquiring more experience with relevant studies.
Uncertainties in cyclostratigraphy in the light of the CIP The reconstructed total signal durations clustered around the exact solution for Cases 1 and 3 (Figs. 5A, 7 A), complemented by a handful of outliers. For Case 2, where the data contained a hiatus, the total duration estimates have a bimodal distribution (Fig. 6A ; see Section 3. 2). The CIP results further show that cyclostratigraphers rather poorly self-estimate the uncertainties of their analysis, as only a weak correspondence is observed between self-estimated uncertainty and the actual offset of the submitted result with the true duration. This might also be due to the limited available stratigraphic constraints that were provided. In Case 1, the accuracy of the proposed duration was systematically higher when the methodology relied on visual analysis rather than on statistical approaches.
Flexible guidelines for an effective cyclostratigraphic study The formulation of strict guidelines for cyclostratigraphy is untenable. Some archives and proxies are more suitable than others for identifying an astronomical imprint (e. g. Li et al. , 2019). Each combination of proxy, archive, geologic age and environmental setting requires a tailored approach, limiting the applicability of a strict set of rules. Nonetheless, the members of the cyclostratigraphic community that gathered as part of the CIP highlighted the need for a systematic use of definitions and handling protocols, e. g. potential hiatuses, data processing before spectral analysis, reporting on bandpass filter algorithm and settings, motivation for choosing a tuning target, definition of the precise stratigraphic locations of cycle extremes and an overall uncertainty assessment.
All of these are important for improving reproducibility in cyclostratigraphy. As an attempt towards increased consistency in cyclostratigraphic approaches, we have formulated a general set of flexible guidelines that an effective cyclostratigraphic study should aspire to follow. These guidelines reflect lessons learned from the CIP experiment, and are complemented with some additional steps contributed by the participants. This process can be flexible and iterative, and so the following steps can be rearranged and repeated. reference to allow for the reproducibility and/or revision of astrochronologic models. When feasible, computational scripts used for data processing and astrochronology development should be made available for rapid duplication and updating. In addition to these guidelines, which can be applied to every cyclostratigraphic study, we highlight additional steps that can further reduce uncertainty of cyclostratigraphic interpretations.
Cyclostratigraphy is still a relatively young and fast-growing geological discipline. Hence, relevant teaching and training is often missing from geology curricula. We advocate and support further efforts for the inclusion of cyclostratigraphy and the principles of astronomical climate forcing in training programs, both practically and theoretically. To support advances in training capabilities from within the cyclostratigraphic community, the CIP workshop has proposed several ideas: 1) a more formal organization of the community, 2) a centralized resource with information and teaching tools, 3) the need for openly available raw data -and data processing details -supporting publications, 4) a database for (published) cyclostratigraphic research and 5) more regular community gatherings, as this project and workshop turned out to be instructive for all participants.
Acknowledgments The  Cyclostratigraphy Intercomparison  Project (CIP) and Workshop are supported by the  Royal Flemish Academy of Belgium for Science and the Arts (KVAB) ,  Research Foundation -Flanders (FWO) ,  Fund for Scientific Research (FNRS) ,  International Association of Sedimentology (IAS) ,  Earthrates  ( National Science Foundation ) and the  Vrije Universiteit Brussel (VUB) -Hooverfonds. Matthias Sinnesael thanks the  Research Foundation of Flanders (FWO)  for the awarded PhD Fellowship (FWOTM782). We thank editor  André Strasser  and reviewers  Mathieu Martinez  and  Graham Weedon  for constructive feedback.
Bleeding events related to surgical procedures were also included. 14 e Statistics and Data Management Center for the POINT trial, located at the Medical University of South Carolina, developed a web-based clinical trial management system (CTMS) for the study. It included a 6-step outcome adjudication module (Figure), which automated the coordination of adjudication activities by controlling workflows, in real time, based on data collected in the CTMS. 12 te investigators reported all suspected outcome events using prespecified standardized definitions described in the protocol and listed on the case report form (eAppendix 2 in the Supplement) and prepared and uploaded an event packet for each suspected end point, with translations provided as needed. The packet consisted of a standardized checklist (eAppendix 3 in the Supplement) and redacted copies of discharge summaries, consultation notes, head imaging reports, and laboratory values for both the index and outcome events, as well as a concise clinical narrative summary prepared by the site investigator.
Figure. Six-Step Outcome Adjudication Module Site adds/edits SAE CRF and uploads the event packet System determines adjudicators 1, 2, and 3 based on S2Q2 and the random number R Step S1 = PM Reviews S1Q1: CRF properly completed? S1Q2: Event type? Step S3 = ADJ1 Review S3Q1: Outcome category? S3Q2: Death type? Step S5 = ADJ3 Review S5Q1: Outcome category? S5Q2: Death type? Step S6 = Chair Review S6Q1: Outcome category? S6Q2: Death type? Step S4 = ADJ2 Review S4Q1: Outcome category? S4Q2: Death type? Step SAS software (version 9. 4; SAS Institute Inc) was used to perform all statistical analyses. Statistical significance was set at 2-sided P <. 05.
(3. 17) With the help of the Mathematica package developed in  [30] , we have checked that (3. 13) and (3. 16) agree up to h = 10 exactly in z. We have also checked that the expansion of (3. 16) around z = 0 is in agreement with (3. 4) for generic h up to order z 20. As mentioned at the beginning of this section, we can use these results to derive the monodromies around z = 1 of the Virasoro block at order 1/c from the knowledge of the non-analytic behaviour of the hypergeometric functions. This is particularly straightforward in the case of integer conformal weights where we can use (3. 16) (together with (3. 6) and (3. 8) which are valid in general). A new feature of the 1/c corrected Virasoro block is the presence of terms proportional to log 2 (1 -z), which are absent in the global blocks.
Comparison of PXR and PPARα-dependent transcriptome This prompted us to investigate whether PXR activation influenced PPARα activity. We took advantage of our previously published microarray dataset  [14] , in which C57Bl6/J male mice carrying an hepatocyte-specific deletion of Pparα (Pparα hep-/-) were fasted for 24h to induce Pparα activity and compared to their wild-type littermates (Pparα hep-/-). We have indeed previously shown that, during fasting, PPARα senses increased levels of free fatty acids released from adipocytes, and in response, controls the expression of hundreds of genes involved in fatty acid uptake, transport, and catabolism in hepatocytes  [14, 15]. Figure  3A  indeed illustrates that a large number of the fasting-induced hepatic genes are PPARα sensitive, with 538 genes significantly up-regulated in a PPARα-dependent manner.
Blood and tissue samples Body weight was monitored at the beginning and at the end of experimental period. Prior to sacrifice, the submandibular vein was lanced, and blood was collected into lithium heparin-coated tubes (BD Microtainer). Plasma was prepared by centrifugation (1500 g, 10 min, 4°C) and stored at -80°C. At sacrifice, the liver, the three parts of the small intestine (duodenum, jejunum, ileum), and the colon were removed and snap-frozen in liquid nitrogen and stored at -80°C until used for RNA extraction. Total RNA was extracted with TRIzol reagent (Invitrogen). Gene expression profiles were obtained at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France) using Sureprint G3 Mouse GE v2 microarrays (8x60K; design 074,809; Agilent technologies) following the manufacturer's instructions Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [38]  and are accessible through GEO Series accession numbers GSE123804.
For real-time quantitative polymerase chain reaction (qPCR), 2 μg RNA samples were reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Supplementary Table  1  presents the SYBR Green assay primers. Amplifications were performed using an ABI Prism 7300 Real-Time PCR System (Applied Biosystems). qPCR data were normalised to TATA-box-binding protein mRNA levels, and analyzed with LinRegPCR. v2015. 3.
Statistical analysis Microarray data were processed using R (http://www. r-project. org) and Bioconductor packages (www. bioconductor. org, v 3. 0, Gentleman,  Carey et al. 2004 ). Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath) and normalized using CrossNorm method  [40]. Normalized data were first analysed using Matlab (v2014. 8). Principal component analysis was performed using an in-house function. Linear model was fitted using the limma lmFit function  [41]. Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using Benjamini-Hochberg procedure for False.
Acknowledgments: We thank all members of the  EZOP  staff for their careful help with this project. We thank the staff from the  Genotoul: Anexplo ,  Get-TriX  and  Metatoul-Lipidomic  facilities. Pxr-/-mice are a generous gift from  Pr Steven Kliewer  ( University of Texas Southwestern Medical School, Dallas, TX, USA ). Colony founders were kindly provided by  Pr Urs A Meyer (Biozentrum, University of Basel, Basel, Switzerland ).
Therefore, experimental data on hadron TSSAs measured in p+A collisions with varying A-size will provide valuable information testing these models and bring new insights in understanding the dynamics of the p+A collisions. We report here on the observation of a nuclear dependence of TSSAs of positively-charged-hadron production at forward rapidity (0. 1 < x F < 0. 2 and 1. 4 < η < 2. 4, probing 0. 004 < ∼ x < ∼ 0. 1 in the nuclei) in collisions between transversely polarized protons and unpolarized protons or nuclei, p ↑ +p, p ↑ +Al, p ↑ +Au at √ s N N = 200 GeV measured with the PHENIX detector. The positively charged hadron is preferred in the nuclear-dependence measurement because the significance of TSSAs for negatively charged hadrons will be reduced by the partial cancellation of the asymmetry due to opposite signs of TSSAs for π -and K -in p ↑ +p collisions  [8, 10].
In this measurement, we follow the convention to quantify TSSAs as A N , where A N is the modulation of the azimuthal angle of the hadron (φ h ) relative to the azimuthal angle of the transverse spin of the proton (φ pol ), i. e. , hadron-production cross section σ ∝ 1 + A N sin(φ pol -φ h ). The data from transversely polarized p ↑ +p, p ↑ +Al, and p ↑ +Au collisions at √ s N N = 200 GeV were collected with the PHENIX detector during the RHIC 2015 running period. Proton beams were polarized vertically with respect to the beam direction with an average polarization of 58% (clockwise-beam) or 57% (counterclockwise-beam) for p ↑ +p, 58% for p ↑ +Al, and 61% for p ↑ +Au collisions, with a relative uncertainty of 3% due to uncertainty in the polarization normalization. The beams are bunched. To minimize systematic effects due to time dependence of machine and detector performance, the spin configuration of the colliding bunches is alternated every 106 ns.
ACKNOWLEDGMENTS We thank  Melina Schuh  for providing helpful advice with Trim-away,  Jurrien Dean  for providing ZP2 antibody,  Khaled Machaca  for providing the SNAP25∆20 and SNAP25WT constructs,  Carmen Williams  for providing the pIVT vector,  Jim Watras  for providing IP 3 receptor antibody, and  Laurinda Jaffe  for helpful comments and suggestions.
Two 1 GHz wide sub-bands were observed simultaneously and they were centred at 4. 7 and 6. 9 GHz. These data were calibrated and imaged using the Obit software package. The source extraction has been performed using the BLOBCAT software package and verified through a combination of visual inspection and cross-matching with other radio and mid-infrared surveys. Results. The final catalog consists of 1575 discrete radio sources and 27 large scale structures (including W43 and W44). By crossmatching with other catalogs and calculating the spectral indices (S (ν) ∝ ν α ), we have classified 231 continuum sources as H ii regions, 37 as ionization fronts, and 46 as planetary nebulae. The longitude and latitude distribution and negative spectral indices are all consistent with the vast majority of the unclassified sources being extragalactic background sources.
Calibration, Data Reduction and Imaging of Continuum data The data were calibrated using the Obit software packages  (Cotton 2008) , which was designed for handling radio astronomy data. Obit applications can be accessed through a Python interface  (ObitTalk). Obit also inter-operates with Classic AIPS  (Greisen 2003)  and has access to its tasks. Observations made with the VLA are well standardized, as is the data calibration, and as such calibration pipelines are now available. These implement the data reduction and calibration using amplitude, phase, and bandpass calibrators (for an example consult the CASA pipeline webpage: https://science. nrao. edu/facilities/vla/dataprocessing/pipeline). Following the methodology of these pipelines, we have developed a pipeline in Obit to calibrate the GLOSTAR-VLA data with some differences.
3. A standard VLA calibration was applied in the Obit package which alternates editing of data affected by instrumental problems or interfering signals with various calibration steps. External calibration includes VLA system temperature (T sys ) calibration, group delay, bandpass, amplitude and phase, and polarization calibration. After a Hanning smoothing of the data, an automated search for outliers in the data is performed by a comparison against running medians in time and frequency for each data product. Calibrator data is checked for adequate signal to noise ratio by an RMS/mean test. After the initial pass at data editing, the various parallel hand calibration steps are performed applying further editing based on outliers in the calibration solutions. Following the first pass at calibration, a second set of automated editing steps is done on the calibrated data.
The calibration tables are then reset, the flagging table kept and the calibration is repeated. This assures that the calibration is based on fully edited data. After flagging cross hand outliers, the polarization calibration is performed. Per pointing flagging is also included as part of the imaging. An initial, shallow CLEAN is done to determine the approximate flux density in the field. Data for that pointing is then clipped at 5 times the estimated pointing integrated flux density. 4. The Obit task MFImage was used for the continuum imaging (see  Cotton et al. 2018 for details). First, we imaged the data after a shallow cleaning to identify strong sources that needed to be imaged in outlier fields in the final imaging. This program uses wide-band multifrequency synthesis by dividing the observed bandwidth into frequency bins, i.
Source extraction The source extraction has been performed using the BLOBCAT software package developed by  Hales et al. (2012). BLOBCAT is a Python script that utilizes a flood fill algorithm to detect and identify blobs, or islands of pixels representing sources, in two- dimensional astronomical radio-wavelength images. The package has been successfully used to create the source catalog for The HI/OH/Recombination line survey of the inner Milky Way (THOR;  Beuther et al. 2016 ) survey, and has been adopted by us to facilitate the comparison with this complementary VLA continuum and line survey. We have followed the same method used by the THOR team at 1. 4 GHz, which is outlined in  Bihr et al. (2016). The GLOSTAR radio continuum image has positiondependent noise and so we need to produce an independent noise map of the region to perform the automatic source extraction.
We have also made no attempt to separate the emission in cases in which a bright compact source is coincident with larger scale structures as there is not enough information to split them into reliable components as any measurement will be highly uncertain. There are a total of 51 of those cases. We have, however, separated the emission into two or more sources where the overlap is small and the integrated flux density can be reliably determined (see lower panel of Fig. 5  as an example). Inspection of the GLIMPSE 4. 5, 8. 0, and 24 µm mid-infrared images of these blended sources identified 20 cases for which the emission peaks appear to be associated with distinct sources and could be separated. We split them into two or three sources according to the number of infrared counterparts. We use the CASA software to select and split the regions in which emission is above 4σ and then calculate their parameters such as flux and size from their emission area.
For some sources, e. g. uniform and compact radio emission, we make a Gaussian fit using the task imfit also from the CASA software suite. An example of these type of sources is shown in the lower panel of the Fig. 5. This process resulted in 44 sources being identified from the 20 blended sources examined. These split sources are treated in the source catalog (see Table  7 ) as individual sources but we also include a flag to the entry so these can be easily identified. The peak and centroid position values are considered the same for these cases.
8/1. 5) , where S THOR and S GLOSTAR are the peak flux densities at 6 cm and 20 cm, respectively. The uncertainty is calculated using: ∆α = (σ THOR /S THOR ) 2 + (σ GLOSTAR /S GLOSTAR ) 2 ln (5. 8/1. 5) where σ GLOSTAR and σ THOR are the uncertainties in the peak flux densities. For a comparison of both methods discussed above, we selected sources with reliable GLOSTAR internal spectral indices, σ α < 0. 2, and compare them with their GLOSTAR-THOR spectral indices (see Fig. 15 ). A linear fit to these values yields that the slope is m = 0. 94 ± 0. 03, close to the slope of the equality line. In conclusion, the spectral index values derived with the two methods are statistically similar. The spectral index derived from GLOSTAR and THOR data combination and their associated uncertainty are given in Cols.