dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology The operational implementation of the class 4 inter-comparison requires all partners to access an identically formatted set of observations such that metrics derived from the match-ups can be directly compared. For a particular physical parameter a single centre is responsible for assembling and disseminating the observations related to that parameter on a daily basis to the other participating centres. At present the Met Office are responsible for assembling Argo data extracted from MyOcean2 (www. myocean. eu), in-situ SST data retrieved from USGODAE and altimeter data from AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data). In the future, Environment Canada will make sea ice observations available in the same manner. Once the observations have been assembled each participating partner is then responsible for interpolating their forecast counterparts to the observation locations.
Quality control All observations underwent a quality screening process before being included in the inter-comparison in order to ensure closeness to the truth. In-situ SST observations taken from USGODAE were filtered by examining their quality information and rejecting observations with probability of gross error greater than 0. 5. Although USGODAE catalogue many types of in-situ SST observations, the data type selected for this inter-comparison was 'DriftingBUOY'. These observations are the most numerous of the in-situ SST types and are considered to be the most reliable. They report multiple times per day, so the diurnal cycle in surface temperature may be aliased in some cases. The diurnal cycle is not present in the model counterparts since they are daily mean values. Argo profiles were extracted 5 days behind real-time via MyOcean2.
These profiles underwent an automatic quality control check before being uploaded to the MyOcean2 data servers. However, occasionally profiles exhibiting gross errors made it through the automatic screening. These profiles were identified by comparing all observations to climatology and removing those which differed from climatology by more than 10 standard deviations of the long term climatological error. This additional quality check resulted in approximately 1 profile in every 10000 profiles being rejected. The quality of altimetry data received from AVISO is determined through the Met Office Observation Processing System (OPS).
Name Geographical limits Global Ocean 90S -90N 180 W -180E North Atlantic 0N -70N 100 W -31E Tropical Atlantic 20S -20N 70 W -30E South Atlantic 60S-0S 70 W-30E North Pacific 0 N -65N 100E -77 W Tropical Pacific 20S -20N 90E -70 W South Pacific 60S -0S 100E -70 W Indian Ocean 40S -31N 20E -120E BlueLink 70S -20N 90E -180E resolving resolution of 1/12 degree. RTOFS Global is part of a larger national backbone capability of ocean modeling at NWS in a strong partnership with the US Navy. The forecast system is run once per day and produces forecasts of up to 8 days. RTOFS Global utilizes the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3DVAR data assimilation methodology  (Cummings 2005). The model grid is on a Mercator projection from 78.
External recordings of the electrohysterogram (EHG) can provide new knowledge on uterine electrical activity associated with contractions. Better understanding of the mechanisms underlying labor can contribute to preventing preterm birth which is the main cause of mortality and morbidity in newborns. Promising results using the EHG for labor prediction and other uses in obstetric care are the drivers of this work. This paper presents a database of 122 4-by-4 electrode EHG recordings performed on 45 pregnant women using a standardized recording protocol and a placement guide system. The recordings were performed in Iceland between 2008 and 2010. Of the 45 participants, 32 were measured repeatedly during the same pregnancy and participated in two to seven recordings. Recordings were performed in the third trimester (112 recordings) and during labor (10 recordings).
Guided by this preliminary work, the 16-electrode system was used to perform pregnancy and labor recordings in Iceland at Landspitali University Hospital, Akureyri Hospital and the Akureyri Primary Health Care Centre. In total, a database of 122 EHG recordings was created between 2008 and 2010. The majority of participants were measured multiple times during the same pregnancy and took part in two to seven recordings. The e multiple, or longitudinal, measurements were aimed at observing the evolution of contractions during pregnancy and towards labor. Parts of the data have already been used for developing and analyzing various signal processing methods and have led to several publications  8, [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. In particular, the work has concentrated on efforts to accurately distinguish true labor contractions from normal pregnancy contractions, with some success  8, [10] [11] [12] [13] [14] [15] [16] [17] [20] [21] [22] [23].
Data collection The recordings were performed between 2008 and 2010 in Iceland. Pregnancy recordings, defined as recordings performed at antenatal care clinics on participants in the third trimester and not suspected to be in labor, were performed at Akureyri Primary Health Care Centre and Landspitali University Hospital. Labor recordings, defined as measurements performed on participants suspected to be in labor, present in the labor wards and who delivered within 24 h, were performed at Landspitali University Hospital and Akureyri Hospital. Participants were invited to take part in the recordings during antenatal care visits or at the labor wards and had normal singleton pregnancies and no known risk factors for preterm birth. Informed consent was obtained from every participant and the protocol was approved by the National Bioethics Committee in Iceland (VSN 02-006-V4).
The measurements were performed using a sixteen channel multi-purpose physiological signal recorder (Embla A10), most commonly used for investigating sleep disorders. An anti-aliasing filter with a high cut-off frequency of 100 Hz was used but no high pass filter was used. The signal sampling rate was 200 Hz and the signal was digitized to 16 bits. The sixteen monopolar electrode signals were originally stored in the EDF (European Data Format) format by the Somnologica software used to control the Embla A10. All the recordings were performed by the same person (A. A. ). Each participant was assigned an ID number and for each recording, information on participant age, body mass index (BMI) gestational age, placental position, gravidity, parity, history of cesarean section, eventual mode of delivery and gestational age at delivery was noted.
Data processing No processing of data was performed beyond converting between file formats. The aim is that those that use the data can do so with a fresh start. Future additions to this database may include pre-treated signals and segmented contractions. The EDF files obtained during the recordings were converted into WFDB (WaveForm DataBase--www. physionet. org/physiotools/wfdb. shtml) compatible signal (. dat) and header (. hea) files using the edf2mit WFDB application (www. physionet. org/physiotools/wag/edf2mi-1. htm) and Cygwin software (cygwin. com). Annotation (. atr) files containing events during recordings were created manually by using the WFDB-compatible signal and header files and WAVE, an X Window System client application (www. physionet. org/physiotools/wfdb. shtml#WAVE). The wfdb2mat WFDB application (www.
physionet. org/ physiotools/wag/wfdb2m-1. htm) was used to convert the WFDB-compatible signal and header files into MATLAB (. mat) and corresponding header (. hea) files. The rdann function from the WFDB Toolbox for MATLAB (www. physionet. org/physiotools/matlab/wfdb-app-matlab) was used to create MATLAB  (. mat) files containing the information from the annotation files. The tocograph paper traces were scanned to JPEG images and a time axis corresponding to the recording was inserted onto the scanned images. All information that could possibly lead to the identification of the participant, such as personal data and dates, was manually removed from these images using a graphics editor.
Data Records The data records in the Icelandic 16-electrode electrohysterogram database are stored in a PhysioBank database in PhysioNet  28  (Data Citation 1). A total of 122 recordings were performed on 45 participants. Of the 45 participants, 32 were measured more than once during the same pregnancy and the highest number of recordings for a participant was seven recordings. Ten recordings were performed during labor and five participants took part in measurements during both pregnancy and labor. The lowest gestational age was twenty nine weeks and five days (29w5d-pregnancy recording) and the highest gestational age was forty one weeks and five days (41w5d-labor recording). The average recording duration for pregnancy recordings was 61 min (range 19-86 min) and the average recording duration for labor recordings was 36 min (range 8-64 min).
Each recording has three associated files: -A scanned tocograph with a manually inserted recording time axis (. jgp file). Each small square represents 30 s. -A binary signal (. dat) file containing the data from the 16 monopolar electrodes. -A header (. hea) file: The fields in the header files are according to the WFDB convention and are listed in detail on the PhysioNet website (www. physionet. org/physiotools/wag/header-5. htm). Lines 2-17 of the header files are signal specification lines and the strings at the end of these lines correspond to the signal labels. The signals are labelled with 'EHGn' where 'n' refers to the relevant electrode number (electrode numbers are not in ascending order). Information from each recording is at the end of the header files and includes: - For 111 of the recordings, there is also an annotation (.
atr) file containing the type and timing of events. The types of event are: -C-Contraction. Used when the participant feels a contraction or there is a very likely contraction on the tocograph (not always used when there is an obvious contraction on the tocograph). -(c)-Possible contraction. Used when there is not a very likely contraction but the participant has pressure sensation or a contraction is suspected on the tocograph. -pm-Participant movement. -pos-Participant change of position. -fm-Fetal movement. Used when the participant feels fetal movement. -em-Equipment manipulation. Used when electrodes are pressed more firmly onto the abdomen if otherwise not explained in the comments. The database also includes: -The zip file icelandic16ehgmat. zip that includes MATLAB (. mat) versions of all the signal files along with header files (file names of the form ice###_*type*_*record number*m) and MATLAB (.
mat) versions of the annotations (file names of the form ice###_*type*_*record number*m_ann). This is provided for the convenience of users that want to analyze the data in MATLAB. -RECORDS. txt containing a list of the recordings by record name, with one record name per line. Table  1  (available online only) contains the clinical information from each recording (information from the header files excluding comments) along with the recording duration and whether or not the recording has a corresponding annotation file. This information can also be found in info. txt in the database.
Technical Validation The EHG signal has been shown to be representative of uterine contractions  6  and EHG is, in general, a well-proven technique  5. The proof of concept and technical validation for the recording method used for the database was made in a preliminary study in 2007 (ref. 9). The preliminary study applied recognized EHG techniques to a new recording setup involving a 4-by-4 grid. This was mainly to better observe and analyze the spatial characteristics and propagation of the electrical activity during contractions rather than just the activity in a single location of the uterus as had been done before  2, 6. The results from the preliminary study showed a very acceptable SNR (signal to noise ratio) of bipolar signals (difference between adjacent monopolar electrodes)  29.
The use of the monopolar signals singly was however considered problematic, even with adaptive filtering methods due to external common mode noise (maternal ECG, respiration movements etc. ). Efforts to use recent techniques such as empirical mode decomposition (EMD) and canonical component analysis (CCA) to clean up the signal have since met with some success  30. The preliminary study data was also used to present a moving picture of the electrical activity during contractions. The activity was clearly observed and correlated well with the simultaneous tocograph trace  9. In the preliminary study, electrodes were placed one at a time for the 4-by-4 grid, which was a timeconsuming task and achieving the desired inter-electrode distance required great operator precision. To address these issues, a placement guide system was specifically developed (described in Methods).
The system has a standardized setup ensuring a consistent distance between electrodes. The database therefore contains recordings made in a very similar way to the preliminary study, with the same electrode configuration, the same electrodes and same recording device but with a slightly smaller inter-electrode distance (17. 5 versus 21 mm). The system has a shorter electrode attachment time than for the one by one electrode attachment method from the preliminary study and the placement of the electrodes into the silicone backing can be done before a recording, shortening the setup time for an actual recording to around five minutes. This enables recordings to be performed when there is little time and reduces any inconveniences for participants and health professionals. The data from the preliminary study is not included in the database presented here.
dat files and so there is an index number discrepancy of one between the two file formats (. mat and. dat). The MATLAB files contain the absolute raw units. Division by 131. 068 gives the physical units in mV. Even though some pregnancies ended in cesarean, the participant was on occasion already in spontaneous labor. These incidences are explained in the comments sections of the header files. If a recording was close to birth, then the timing of the recording with regard to the birth is in the comments of the header file. Our aim is to publish this database as is, without giving the user any detailed directions and thus encouraging open-minded exploitation of the data. Making raw data available is the best way to do this. There are however some pointers about how to make sense of the data which we would like to communicate to the users.
There can be differences in when participants start to feel a contraction or fetal movements and so differences in when participants notify about events. Events were therefore occasionally approximated to the nearest whole minute. There are also delays internal to the tocodynamometers. Due to factors such as these, the inserted recording times on the tocographs and the annotation times may be up to ± 30 s from the actual recording or event times. 3. A user doing intensive work using these signals will have to develop an effective methodology to keep track of the signals, the way they have been pre-treated and the associated clinical parameters. Inspiration on how to organize such work can be sought in the structure of the SQL database framework which we developed  31. Users can view the data and annotations through two web interfaces: LightWAVE (a JavaScript viewer-www.
physionet. org/lightwave) and ATM (Automated Teller Machine-www. physionet. org/cgibin/atm/ATM). ATM´s toolbox includes software that can convert WFDB signal files to text, CSV, EDF or. mat files and can show samples and annotations as text. WAVE can also be used for viewing and analyzing the signals (www. physionet. org/physiotools/wfdb. shtml#WAVE). Annotations can be opened by using the '-a' option with the wave WFDB application (www. physionet. org/physiotools/wag/wave-1. htm). The WFDB Software Package can be used to work with the recordings (www. physionet. org/ physiotools/wfdb. shtml). For example, the command 'rdann -r *record name* -a atr -f 0' can be used to read the annotation files (www. physionet. org/physiotools/wag/rdann-1. htm). The WFDB Toolbox for MATLAB and Octave can also be used to work with the recordings (www.
Acknowledgements The  Icelandic Centre for Research (RANNIS)  supported this work. Óskar Þór Lárusson  and  Snorri Már Snorrason  assisted with the development of the placement guide system. Shirin Najdi  and  Arnaldo Batista  modified the rdann function from the WFDB Toolbox for MATLAB, which enabled the reading of annotation information from this database. The authors of this manuscript had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. www. nature. com/sdata/.
We thus identified a gene set in which epigenomic variation reflected differences in the lifestyle and habitat, as well as in genetic background, of RHGs and AGRs, regardless of their geographic location. Because recent DMS were found to be particularly enriched in functions related to immune processes, we next evaluated the extent to which potential variability in blood cell proportions, despite our adjustments for cell count heterogeneity (Supplementary Note 3), may still affect our findings. No major differences in immune cell counts were observed between the populations compared (Supplementary Fig. 8 ; Supplementary Note 4). Furthermore, when using a 'filtered' data set, in which we removed a set of 51,386 probes that have been shown to correlate with cell counts by an independent study  36  , we found that the biological functions associated with recent and historical DMS clearly differed and were primarily associated with host-pathogen interactions/cellular processes and development, respectively (Supplementary Table  5 ; Supplementary Note 4), confirming the results obtained using the global data set.
We thus provide a basis for further experimental and theoretical studies assessing the role of epigenetic mechanisms in human adaptation over different time scales. genotyped using the Illumina OmniExpress for 719,665 SNPs. We filtered out 7,120 SNPs on the basis of their physical location (that is, those on the Y-chromosome and SNPs unmapped on dbSNP build 37), problematic genotype clusters in GenomeStudio (Illumina, San Diego) based on a GenTrain score o0. 35, and SNP call rate o95%. We also filtered out two w-RHG individuals with a call rate o95% and eight individuals presenting cryptic relatedness (that is, kinship coefficient 40. 15 with another individual), with the KING program  54. We phased the 191 Omni1 individuals with SHAPEIT2 (ref. 55) and imputed missing SNPs in the OmniExpress data set, using the Omni1 data set as a reference, with IMPUTE2 (ref.
56). Five samples (4 w-RHG and 1 f-AGR) with call rates o95% after imputation were removed. After filtering out low-quality imputed SNPs and SNPs with call rate o95% after imputation, we obtained a final set of genotypes at 876,886 SNPs for 347 individuals, comprising 98 w-RHG, 94 w-AGR, 60 f-AGR, 47 e-RHG and 48 e-AGR individuals. To evaluate imputation accuracy, we compared the concordance of genotyped and imputed SNPs with wholegenome sequences from 20 w-RHG (Baka) and 20 w-AGR (Nzebi) studied here, obtained by Illumina HiSeq 2000 at an average coverage of 5. 6 Â (17,080,726 SNPs, unpublished data). SNP calling of next-generation sequencing data was performed with GATK  57. We kept SNPs passing a sensitivity threshold (VQSR tranche) of 99. 9%, with a confidently called reference allele, passing Hardy-Weinberg equilibrium and found in genomic regions of 'strict callability' (as defined by the 1000 Genomes Project Consortium  58  ) and limited evidence of identity-by-descent (IBD).
Average concordance rate was 97. 2% (individual range: 94. 6-99. 6%) and 96. 5% (individual range: 94. 2-98. 6%) for genotyped and imputed SNPs, respectively. Finally, we had to remove another two individuals because of their methylation profiles (see the 'DNA methylation data processing' section), yielding a final data set of 345 individuals for whom we had both genotype and methylation data. Genome-wide DNA methylation analysis. Genome-wide DNA methylation data at more than 485,000 sites was obtained using an Infinium HumanMethylation450 BeadChip. Bisulfite conversion of 750 ng of genomic DNA was performed with the EZ DNA Methylation Kit. Successful conversion was confirmed by methylationspecific PCR before proceeding with subsequent steps of the Infinium assay protocol. The bisulfite-converted genomic DNA was isothermally amplified at 37 °C for 22 h, enzymatically fragmented, purified and hybridized with the HumanMethylation450 BeadChip at 48 °C for 18 h.
Each BeadChip was then washed to remove any unhybridized or non-specifically hybridized DNA. Labelled single-base extension was performed with bead-bound probes hybridized to the DNA, and the hybridized DNA was removed. The extended probes were stained with multiple layers of fluorescence, and the BeadChip was then coated with a proprietary solution and scanned with the Illumina iScan system. Raw data were processed with Genome Studio Methylation Module software. Targeted pyrosequencing. Bisulfite PCR-pyrosequencing assays were designed with PyroMark Assay Design 2. 0 (Qiagen). The regions of interest (IGF2BP2 cg23956648, HOXC6 cg21582112, ZNF492 cg09314196, 6p12. 3 enhancer region cg23053977, DOCK1 cg06406458, COL23A1 cg08684511, RORA cg09879458, and ADAM28 cg18757155) were amplified by PCR, using the HotstarTaq DNA polymerase kit (Qiagen) as follows: 15 min at 95 °C (to activate the Taq polymerase), 45 cycles of 95 °C for 30 s, 58 °C for 30 s and 72 °C for 30 s, with a final 5-min extension step at 72 °C.
For pyrosequencing, a single-stranded DNA was prepared from the PCR product with the Pyromark Vacuum Prep Workstation (Qiagen), and sequencing was performed with sequencing primers on a Pyromark Q96 MD pyrosequencer (Qiagen). Methylation levels were calculated for each CpG dinucleotide with Pyro Q-CpG software (Qiagen). The primer sequences are listed in Supplementary Table  7. DNA methylation data processing. In total, 381 samples were hybridized with the HumanMethylation450 array, including 362 unique samples and 19 technical replicates. We removed probes that potentially cross-hybridize  59  , those on the X and Y chromosomes, and those containing SNPs, or associated with CpGs containing SNPs, at a frequency higher than 1% in at least one of the studied populations. The list of SNPs was based on (i) our own genotyping data set for more than 876,886 SNPs genome-wide (see 'Genotyping data' section), and (ii) the whole-genome sequencing data set for 20 w-AGR and 20 w-RHG individuals mentioned above.
Following this filtering process, 365,886 of the original 485,512 sites on the array were retained. We calculated methylation levels from raw data, using the R bioconductor lumi package. The M-value has been shown to provide better detection sensitivity than b-values at extreme levels of modification  34. We therefore used the M-value unless otherwise stated. M-values were then adjusted for background and colour bias with lumi, and quantile normalized. We corrected for technical differences between Type I and Type II assay designs, by performing subset-quantile within array normalization on M-values with the R bioconductor minfi package  60. PCA showed that a batch effect explained part of the variance (Kruskal-Wallis P value of 8. 35 Â 10 À 55 for PC2) of the normalized data, and we used the ComBat function from the sva bioconductor package to correct for this effect  61.