dataesr/scientific-paragraphs-categorization huggingface dataset:
We set N ANC = 4000 for both the Yoruba and Japanese simulation. At the end of each simulation, we output the full population and computed the number of derived alleles for each individual across a fixed number arbitrarily set to 40,000 SNPs, subsampled from all SNPs. These 40,000 SNPs were subsampled individually for each replicate simulation. The SFS of the population was subsampled to 10 individuals (i. e. 20 haploid genomes) following  Nielsen et al. (2005)  as p i;20 ¼ k À1 P k j¼1 f j i n j À f j 20 À i = n j 20 , where k is the total number of SNPs in the dataset, and n j and f j are the number of haploid genomes in the full sample and the number of derived alleles in the full sample at the jth SNP, respectively (see also  Liu et al. , 2017). We demographic inferences The following previously published datasets were used:.
Acknowledgements We thank  Anthanasios Kousathanas ,  Guillaume Achaz ,  Etienne Patin ,  Lluis Quintana-Murci ,  Sylvain Gle  ´min, and  Rasmus Nielsen  for informative discussions on the subject, and  Montgomery Slatkin  for his careful reading and helpful comments on the manuscript. FP and SA have been supported by a  Swiss NSF  grant No  310030B-166605  to LE. LE was also supported by the  Institut Pasteur in Paris  and by a  Visiting Miller Professorship grant  from the  University of Berkeley  during his sabbatical. Calculations were performed on the UBELIX (http://www. id. unibe. ch/hpc) cluster of the  University of Bern. The source code and setting parameters to reproduce the analyses are available at http://datadryad. org/review?doi=doi:10. 5061/dryad. t76fk80.
07 and 0. 09 • C  (Romanovsky et al. , 2017). Continuous permafrost underlies the unglaciated coastal areas to a depth of about 100 m and the active layer thickness (ALT) at the end of summer ranges between 1 and 2 m  (Humlum, 2005). ALT data are available from the Global Terrestrial Network for Permafrost (GTNP) database (http://gtnpdatabase. org/) for several locations on Svalbard but for different time periods and distances between 25 and 115 km away from Bayelva. The site with the longest record, Janssonhaugen, is located 115 km to the southeast and had an average thaw depth of 175 cm for 1998-2013. The region has experienced increases in cloudiness, precipitation, and the number and intensity of cyclones in recent years, especially during the winter months  (Hanssen-Bauer and Førland, 1998; Sepp and Jaagus, 2011).
Weather station data Meteorological data have been recorded at the AWIPEV research base in Ny-Ålesund since 1992, including radiation component data for the worldwide Baseline Surface Radiation Network. The AWIPEV meteorological data are described by  Maturilli et al. (2013)  and can be downloaded from PANGAEA (http://bsrn. awi. de/data/ data-retrieval-via-pangaea/). Climate records covering a longer period of time are available from Svalbard since 1898, with the establishment of a permanent weather station in 1911  (Førland et al. , 2011). Since the Bayelva site is located close to the leading edge of the Brøggerbreen glacier, a different local micro-climate can be expected from Ny-Ålesund. A climate station was therefore installed in 1998, within the fenced area. The temporal resolution of the meteorological measurements was increased in October 2009 from 60 to 30 min.
Soil profile sensor installation 1998 Pairs of soil temperature and soil water content sensors were installed over a cross section in 1998; their positions are shown in Appendix C (Fig. C8 ). The probes automatically recorded temperatures from that time until January 2012. The temperatures were measured using CSI (Campbell Scientific Ltd. ) thermistors connected to a CR10X data logger with an AM416 multiplexer. The voltage measured for each sensor was stored in a database. Temperatures were calculated from the raw data using the Steinhart-Hart equation  (Steinhart and Hart, 1968)  and sensor calibration at 0 • C (Table D2). The average accuracy determined through calibration was 0. 01 • C with a precision better than 0. 002 • C. However, the max deviation at 0 • C was 0. 1 • C. After installa-tion, the sensors cannot be recalibrated, certainly not without a lasting physical disturbance of the profile.
Soil profile sensor installation 2009 Pairs of soil temperature (CS107) and water content (TDR) probes were installed during August 2009, close to the first profile that was installed in 1998 (Appendix C, Fig. C9 ). Soil temperatures were measured at 11 depths in a profile from the surface down to a depth of 1. 4 m. The data quality from the 2009 system improved considerably compared to the 1998 system, probably due to the data logging and multiplexer system used (CR10X from 1998 to 2011; CR1000 since 2009). Data collection is ongoing.
Soil dielectric number, volumetric liquid water content and bulk electrical conductivity Soil profile sensor installations 1998 and 2009 Time-domain reflectometry probes were installed horizontally adjacent to the temperature probes installed in soil profiles in 1998 and 2009 (see above and Appendix C, Table  C5 ). These TDR probes automatically recorded hourly measurements of bulk electrical conductivity (BEC) and the dielectric number (also referred to as relative permittivity), obtained by measuring the amplitude at very long times and the L a /L ratio (ratio of the apparent to real probe length, corresponding to the square root of the dielectric number). From 1998 to 2008 the measurements were obtained using a Tektronix 1502B cable tester connected to a CR10X data logger and custom-made triple-wire 24 cm probes.
Since 2009 a CSI TDR100 reflectometer has been used, connected to a CR1000 data logger and 30 cm TDR CS605 probes (Campbell Scientific Ltd. ). All TDR probes were calibrated for probe offset following the method described in Heimovaara and de  Water (1993)  and the CSI TDR 100 manual (http: //www. campbellsci. de/tdr100). The dielectric number data and computed volumetric liquid water in frozen and unfrozen soil are provided with the data set. The calculation for volumetric liquid water content takes into account four phases of the soil medium (air, water, ice, mineral) and uses the mixing model from  Roth et al. (1990; Appendix D, Sect. D1). Time-domain reflectometry can also be used to measure the impedance Z ( ) of the bulk soil, which is related to the soil's bulk electrical conductivity.
These data were used to infer the electrical conductivity of soil water and solute transport in the non-sorted circle over a period of 1 year  (Boike et al. , 2008a). The impedance can be determined from the attenuation of the electromagnetic wave traveling along the TDR probe after all multiple reflections have ceased and the signal stabilized. The bulk electronic conductivities were recorded hourly using the TDR setup described in this section. The equipment installed in 1998 was calibrated in pure water, in air and in NaCl solutions with various known conductivities and is corrected for temperature (25 • C), following the method by  Heimovaara et al. (1995). The TDR equipment installed in 2009 was not calibrated for BEC and a probe con-stant of 1 was used for BEC waveform retrieval; Campbell Scientific Ltd.
Permafrost and active layer temperature A 9. 3 m borehole was drilled using a rotary drill rig on 30 March 2009 and cased with PVC. No stratigraphic information or drill core was recovered. A thermistor string was installed, with one sensor above the ground surface and nine from the surface down to 9 m depth (at 0, 0. 5, 1. 0, 1. 5, 2. 5, 3. 5, 5. 5, 7. 5 and 9 m below ground surface, Appendix C, Fig. C10 ). The casing was left open (not refilled) so that the thermistor chain could be retrieved or replaced. Because of instrument failure, thermometers were retrieved and replaced several times. From August 2009, a Geoprecision M-Log5W data logger and sensor chain were used. Temperatures were recorded at hourly intervals, with no averaging; no data was recorded between 30 January and 29 March 2011, due to a low battery voltage.
Data quality control and availability The primary purpose of quality control of observational data is to detect missing data, errors in the data, and possibly to correct errors in order to ensure the highest possible standard of accuracy and optimal use of the data by the broadest range of possible users. We differentiate between different data levels, from online data (http://www. awi. de/im-fokus/ permafrost/direkter-draht-in-den-permafrost. html) to level 2 data (Table  3 ). Data supplied with this paper are level 1 and level 2 data. Level 1 data have undergone an extensive quality-control and are flagged with regards to maintenance periods, physical plausibility, spike and constant value detection and sensor drifts (Table  3 ). Level 2 data are compiled for special purposes such as a combination of data series from multiple sensors and gap-filling of data (documentation of source data is supplied in the PANGAEA data archive).
As an example, our temperature data have been checked through the fall zero-curtain effect and thus provide information on any reduction in accuracy (see Table  3 , Flag 7). These checks are mandatory when small warming trends are to be estimated and interpreted. The selection of flagged data depends on its application and the required accuracies. The data from this Bayelva site have been widely used for the development and evaluation of land surface models (e. g. ,  Chadburn et al. , 2017; Ekici et al. , 2014 Ekici et al. , , 2015)). The newly collated data set will allow multi-year model runs, with improved quality control and checks. The data set presented herein is freely available, either as a download from PANGAEA (https://doi. org/10. 1594/PANGAEA. 880120) or from the Supplement of this paper.
The data are provided as ASCII files. Other available data sets include a flux data set in the European Fluxnet database  (Lüers and Boike, 2013)  and aerial imagery of the snow cover  (Westermann et al. , 2015). Volumetric soil liquid water content and snow cover data are transferred in real time to the NASA SMAP mission (https://smap. jpl. nasa. gov/) and are uploaded to the PAN-GAEA archives on an annual basis.
Data availability The entire data set is available through the following data provider and links: https://doi. org/10. 1594/PANGAEA. 880120 (time series) https://doi. org/10. 1594/PANGAEA. 884730 (HRSC-AX data products) https://zenodo. org/record/1139714 (time series) https://zenodo. org/record/1145373 (HRSC-AX data products) The time series data are also available through the supplement of this ESSD paper.
, 2000; Scholten and Gwinner, 2004; Scholten et al. , 2005; Gwinner et al. , 2005 Gwinner et al. , , 2010)). Since 1997 different airborne versions of HRSC have been developed, one of which (HRSC-AX) was used to acquire stereo color images over Svalbard. The principles of HRSC-AX data processing are described by  Gwinner et al. (2006). Data on the camera orientation are reconstructed from a global positioning system inertial navigation system (GPS INS). HRSC-AX has been used in diverse technical and scientific applications (e. g. ,  Gwinner et al. , 1999 Gwinner et al. , , 2000)). The aerial survey covering the Brøgger peninsula took place on 17 July 2008 at around noon, acquiring data over most of the northern part of Brøggerhalvøya (Fig. B3 ). A Dornier Do 228 aircraft from the German Aerospace Center (DLR) was used for the survey, flying at an altitude of ∼ 2800 m.
Experts from audiology and social science worked together. Data was gathered from adults from four countries (India Iran, Portugal, and the United Kingdom). The result shows that for hearing loss, the most important aspects reported by persons with no hearing loss were: assessment and management; causes of hearing loss; communication difficulties; disability; hearing ability or disability; hearing instruments; negative mental state; the attitudes of others; and sound and acoustics of the environment. For hearing aids, the significant aspects were: disability and aging; appearance and design; cost; hearing instruments; and improved hearing and communication. Preliminary examination of the data across countries suggested some cross-cultural differences in the respondents' social representations of hearing loss and hearing aids.
Study design and participants The study involved a cross-sectional survey design. Ethical approval was obtained for each country from local Institutional Ethical Boards prior to data collection (the ethical committee approval included: India 24. 07. 2013; Iran 14/60; Portugal 4433/2013; and the United Kingdom FST/FREP/ 12/325). The four countries were selected to represent the Eastern (i. e. , India and Iran) and Western (i. e. , Portugal and United Kingdom) cultures. The selection of countries was based on ease of data collection as the primary researcher (V. M) had collaborators in those countries. Participants were given detailed study information prior to data collection. Completing and returning the questionnaire was considered as providing consent. The study sample included 404 adults (18 years or older) from the general population of four countries.
Data collection The data were collected using a questionnaire using a free association task. This method has been used access the semantic content of social representation  [20] [21] [22]. In this task the participants are required to report up to five words or phrases that immediately come to mind while thinking about hearing aids. In the next step, they were then asked to indicate whether each word or phrase they had reported had positive, neutral, or negative connotations. In addition, some demographic information (i. e. , age, gender, education, profession, and family history of hearing loss) was also recorded. The original version of the questionnaire was in English and used in the United Kingdom. The questionnaires were translated into Kannada, Farsi, and Portuguese languages, to be used in India, Iran, and Portugal respectively using a wellaccepted method of forward and back-translation  [23].
Data analysis The data were analyzed using various qualitative and quantitative analyses. For the purpose of this manuscript, data from all the four countries were analyzed together treating it as one data set. However, analysis of individual country data, which presents some similarities and differences in responses across countries, has been presented in our recent publication  [18]. In the first instance, the responses from the free association task were analyzed using the qualitative content analysis  [24]. This was followed by cluster analysis made with the Reinert's method used for textual data analysis  [25] [26] [27]. This method is a hierarchical divisive clustering based on a series of bi-partition. Each partition is made with a correspondent analysis. This cluster analysis groups the respondents based on similarities in their responses.
Iramuteq software (http://www. iramuteq. org/) was used to perform both cluster analysis and chi square analysis  [28, 29]. Iramuteq is an open-source text pattern analysis software that is based on R. It is important to note that the clusters were created using the cluster analysis, whereas the chi square analysis was performed to understand what categories were most common in each cluster and also participants with what demographic factors were likely to be in each cluster.
Results The content analysis resulted in 39 categories, which has been reported in detail elsewhere  [18]. Table  2  presents the main categories of responses. Table  2  also includes details about the percentage of responses for each category and the percentage of respondents mentioning each category at least one time. The cluster analysis was performed using the Iramuteq software, which included all the 404 respondents (i. e. , 100% -no outliers were found), and resulted in five clusters (Fig. 1 ). A total of 67 of the 404 respondents (i. e. , 16. 6%) belonged to cluster 1, which was characterized by a central social dimension of the outcome of using hearing aids, hence the cluster is labeled as social aspects (Table  3 ). The cluster includes both positive and negative aspects of the hearing aids.
Top ranking features are recursively selected as the k-best at each round, and finally aggregated via Borda algorithm  [21]. Model performance is computed for increasing number of best ranking features by Matthews Correlation Coefficient (MCC), the measure that better convey in an unique value the confusion matrix of a classification task, even in the multiclass case  [22, 23, 24]. Experiments with randomized features and labels are also performed as model sanity check. We demonstrate Ph-CNN characteristics with experiments on both synthetic and real omics data. For the latter type, we consider Sokol's lab data  [25]  of microbiome information for 38 healthy subjects (HS) and 222 inflammatory bowel disease (IBD) patients. The bacterial composition was analysed using 16S sequencing and a total number of 306 different OTUs was found.
Synthetic data are constructed mimicking the structure of the IBD dataset. They are generated as compositional data from multivariate normal distributions with given covariances and means: in particular, to provide different complexity levels in the classification task, four different instances of data are generated with different ratios between class means. On both data types, the Ph-CNN architecture than compared with state-of-art shallow algorithms as Support Vector Machines (SVMs) and Random Forest (RF), and with alternative neural networks methods such as Multi- Layer Perceptron (MLPNN). Moreover, the bacterial genera detected as top discriminating features are consistent with the key players known in the literature to play a major role during the IBD progression. Since the direct use of Ph-CNN on the IBD dataset leads to overfitting after few epochs due to the small sample size, the IBD dataset is used in a transfer learning (domain adaptation) task.
Experimental setup To ensure predictive power and limit overfitting effect, the experimental framework is structured following the guidelines recommended by the US-FDA led studies MAQC/SEQC  [19, 20]  that investigated the development of predictive models for the analysis of high-throughput data. In particular, the Ph-CNN becomes the core of an experimental setup designed according to the DAP shown in Fig. 3 , based on 10 repetitions of a 5-fold cross validation. In details, the dataset is first partitioned into a non overlapping training set and test set, preserving the original stratification, i. e. , the ratio between sample size across classes. In the experiments described hereafter, the training set size is 80% of the original dataset. Then the training set undergoes 10 rounds of 5-fold stratified cross validation, with Ph-CNN as the classifier and k-Best as the feature selection algorithm, with ANOVA F-value as the ranking score.
The subset of the fused list of ranked featured corresponding to the higher MCC value is selected as the optimal set of discriminating features for the classification tasks. The fused list is further used to build the models for increasing number of features on the validation set (sometimes called the external validation set, to avoid ambiguities with the internal validation sets created at each CV round). Finally, as sanity check for the procedure, the same methodology is applied several times on instances of the original dataset after randomly permuting the labels (random labels in Fig. 3 ) and picking up random features instead of selecting them on the basis of the model performances (random features in Fig. 3 ): in both cases, a procedure unaffected by systematic bias should return an average MCC close to 0.
The IBD dataset The IBD dataset has been originally published in  [25]  for a study aimed at investigating correlation between bacteria and fungal microbiota in different stages of Inflammatory Bowel Disease. IBD is a clinical umbrella term defining a group of inflammatory conditions of the digestive tract, induced by the interactions of environmental and genetic factors leading to immunological responses and inflammation in the intestine: Ulcerative colitis (UC) and Crohn's disease (CD) are the two main conditions. The onset of bacterial dysbiosis of the gut microbiota has recently been observed in patients affected by IBD: a decrease in the abundance of Firmicutes phylum and an increase for Proteobacteria phylum, albeit the exact pathogenesis of IBD remains unknown  [35, 36]. The IBD dataset includes both fungal and bacterial abundances from faecal samples of 38 healthy subjects (HS) and 222 IBD patient, collected at the Gastroenterology Department of the Saint Antoine Hospital (Paris, France).
4%) Dim2 (3. 5%) Groups q q CDf HS q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q -30 -20 CDf HS D 0 D 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 10 0 20 40 Dim1 (36. 7%) Dim2 (5. 5%) Groups q q CDf HS q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -15 -10 using the QIIME 1. 8. 0 software  [37, 38] ; minimal sequence length was 200pb. Sequences are assigned to OTUs using the UCLUST  [39]  algorithm with 97% threshold pairwise identity and taxonomically classified using Greengenes reference database  [40]. Samples with less than 10,000 sequences are excluded from analysis.
The number of different OTUs found is 306: each OTU in the data sets is associated to the sequences with the same taxonomy. Among those sequences, the one with the highest median abundance across samples is chosen as the OTU representative. Since many sequences are not in the Greengenes database, OTUs can have an unassigned taxonomy: in this case, the OTU is removed from the analysis. The actual number of OTUs used in the analyses is 259: for some discrimination tasks, however, the number of features is smaller, since some of them are all zeros for all samples in a class. The distance between the OTUs is inferred first by aligning sequences using the NAST algorithm  [41, 42]  and then by building the phylogenetic tree via the RAxML algorithm  [43]. The phylogenetic tree for the IBD dataset resulting from the described procedure is shown in Fig.
The synthetic datasets The synthetic datasets are generated as compositional data, i. e. , vectors lying in the p-dim Aitchison simplex S = x = (x 1 , x 2 ,. , x p ) ∈ (R + 0 ) p with; p j=1 x j = 1 , whose structure resembles the IBD data. Note that the application of standard multivariate statistical procedures on compositional data requires adopting adequate invertible transformation procedures to preserve the constant sum constrain  [44] : a standard map is the isometric log ratio ilr  [45] , invertibly projecting the p-dimensional Aitchison sim-  plex isometrically to a p -1-dimensional euclidian vector. Transformations like ilr allow using unconstrained statistics on the transformed data, with inferences mapped back to original compositional data through the inverse map. The construction of the synthetic data starts from the IDB dataset, and in particular from the two subsets of the HS and CDf samples (by abuse of notation, we use the same identifier for both the class and the compositional data subset).
2. Fix α ∈ R + 0 and define m HS = µ+ασ µ(HS ) ||µ(HS )|| and m CDf = µ+ασ µ(CDf ) ||µ(CDf )||. Then, define HS' α s as the dataset collecting n HS instances from a multivariate normal distribution with mean m HS and covariance Σ(HS ) and analogously CDf' α s. The two synthetic data subsets HS α s and CDf α s are defined by taking ilr-counterimages: HS α s = ilr -1 (HS' α s ) and CDf α s = ilr -1 (CDf' α s ). Finally, the synthetic dataset D α is then obtained as the union HS α s ∪ CDf α s. Setting the parameter α, we provide different complexity levels in the classification task. For instance, for α = 0 the means of the two classes in the synthetic dataset D 0 are the same, while for α = 1 the means of the two classes HS and CDf are the same as in the IBD dataset; larger values of α correspond to easier classifi-   6.