dataesr/scientific-paragraphs-categorization huggingface dataset:
As such, it also seems acceptable to supplement weather variables from one dataset to another, if not supplied by the latter. This is the case for downwelling long-wave radiation, which is not used by all GGCMs but only by some (Table  4 ) and which is also not supplied by all datasets (Table  5 ). Additionally, not all weather variables have been bias-corrected in the different weather datasets. For some, bias-correction targets are non-existent (e. g. wind speed), for others, the authors of the bias-corrected datasets decided to not bias-correct all variables, such as 2-m temperatures in the WATCH dataset, which was only corrected for elevation after interpolation  132. In contrast, in the WFDEI datasets 2-m temperatures were corrected to CRU temperature averages and diurnal ranges. All bias-correction was applied at the monthly level.
Data Records Data format. Data come in netCDF4 (network Common Data Form 4) files, with a naming convention as in Elliott, et al. 37  , using only lowercase letters in file names, but properly capitalized letters in subfolders. Each file contains only a single output variable. Files are named following the GGCMI convention  37  (Table  6 ): model climate clim scenario sim scenario variable crop timestep start year end year nc4 [ ] [ ] [ ][ ][ ] [ ] [ ][ ][ ]. - -. - - - - - - - - In the data archive, each model has its own subfolder (proper capitalization of model names), which includes a subfolder for each climate dataset simulated, which again contain subfolders for each simulated crop, using the long crop name rather than the abbreviation used in the file name (Table  3 ).
Data availability. Data are available at https://zenodo. org/ (see Online-only Table  4 ). The GGCMs have provided different output sets, covering different climate datasets, crops (Online-only Table  3 ), and output variables (Table  7 ). The overall GGCMI phase 1 dataset at https://zenodo. org/ is structured by GGCM and crop, which have been published as 86 individual packages (Online-only Table  4 ). Given that models have submitted data for different crops, there are 13 individually published datasets for CGMS-WOFOST  [135] [136] [137] [138] [139] [140] [141] [142] [143] [144] [145] [146] [147]  , 6 for CLM-Crop  [148] [149] [150] [151] [152] [153]  , 4 for EPIC-Boku  [154] [155] [156] [157]  , 4 for EPIC-IIASA  [158] [159] [160] [161]  , 2 for EPIC-TAMU  162, 163  , 4 for GEPIC  [164] [165] [166] [167]  , 15 for LPJ-GUESS  [168] [169] [170] [171] [172] [173] [174] [175] [176] [177] [178] [179] [180] [181] [182]  , 13 for LPJmL  [183] [184] [185] [186] [187] [188] [189] [190] [191] [192] [193] [194] [195]  , 4 for ORCHIDEE-crop  [196] [197] [198] [199]  , 4 for pAPSIM  [200] [201] [202] [203]  , 6 for pDSSAT  [204] [205] [206] [207] [208] [209]  , 3 for PEGASUS  [210] [211] [212]  , 4 for PEPIC  [213] [214] [215] [216]  , and 4 for PRYSBI2  [217] [218] [219] [220].
Usage Notes The GGCMI phase1 simulation dataset was conducted with the objective to have as much spatial coverage globally for all crops as possible. As such, crops are also simulated in many regions, where these crops are not currently grown or cannot be currently grown. Growing season data were supplied for an as large area as possible  37  , with the intention to harmonize across models but not necessarily to suggest that cropping is possible during these periods. As such, management, soil and/or weather data at any given site may differ from conditions assumed for the corresponding grid cell and results should mainly be analyzed for larger spatial entities rather than individual sites. Any aggregation or analysis of these data should consider this caveat and either mask currently cropped areas with crop-and irrigation-specific masks  133, 134  or handle and interpret these data with the necessary caution.
Since aggregation masks can affect results 47 these should be selected carefully to fit the intended purpose. Almost all data analyses already conducted focused on crop yields for which models have been evaluated individually and jointly  38. All other output variables have not been evaluated in this context. Generally, all data from the GGCMI phase 1 archive should be subjected to plausibility checks. Analyses that are sensitive to outliers should test for extreme values that are likely to exist in rare cases. It is also advisable to generally assess the range of simulated data when conducting analyses with these data, which can provide an indication of the embedded uncertainty. Despite the semi-automated quality control scripts that tested spatial coverage and data ranges of values in submitted files, not all errors in the output files provided by the modelers could be identified and/or corrected.
Filename tag [] Values [model] pdssat, epic-iiasa, lpjml, etc. (see Table  2 ) [climate] watch, wfdei. gpcc, wfdei. cru, grasp, agmerra, agcfsr, Princeton (see Table  5 ) [clim. scenario] Hist (included for consistency with the ISIMIP file naming convention)  256  [sim. scenario] default_firr, fullharm_noirr, etc. (see Section 2) [variable] yield, pirrww, plant-day, anth-day, etc. (see Table  7 ) [crop] mai, soy, whe, ric, mil, sor, etc. (see Table  3 ) [timestep] Annual (included for consistency with the ISIMIP file naming convention)  256  [start-year]_[end-year] 1958_2001, 1980_2009, 1980_2010, etc. (see Table  5 )  The GSWP dataset is not documented in an article yet, but briefly described at http://hydro. iis. u-tokyo. ac. jp/ GSWP3/exp1. html#initial-conditions (accessed 2018/12/29).
Weather datasets used to drive simulations in GGCMI phase 1. We provide references for all datasets, where available, and an overview of the datasets used to generate these. For details on how the biascorrection was conducted we refer the reader to the corresponding publications. a This denotes the resolution of the underlying reanalysis dataset (and in parentheses the typical resolution of the key target data, temp and precipitation, used in the bias correction). All datasets will be standardized to a 0. 5° × 0. 5° spatial resolution in the GGCMI archives.
The quality of clinical research is undermined by severe misconduct, error, and fraud, which are detrimental to the trust it should arouse. In this perspective article, we show how Blockchain may trace and control processes of clinical trials to prevent these issues or at least discourage them because they would become traceable and averted. Then, we propose a short and doable program in which, amid the complex stream of events in a clinical trial, we select sensitive and misconduct-prone steps that could greatly benefit from Blockchain by its simple core features such as traceability and incorruptibility of data registration or more refined automation tools called Smart Contracts.
BLOCKCHAIN PROPERTIES AND SOME EXAMPLES Blockchain is a secure and distributed datastore or ledger of ordered records of transactions in which incorruptibility of the data is a core feature  (Nakamoto, 2008). With Blockchain, network participants receive a copy of the datastore and can validate by consensus the transactions via a strong cryptographic process, so that trust in transaction validation becomes distributed throughout the network. Hence, parties that do not necessarily trust each other can interact without any third-party intervention, via a shared "tamper-proof " database. By contrast, centralized systems that are widely used datamanagement systems in clinical trials are single points of failure, do not provide strongly trustable traceability logs because they are controlled by a unique stakeholder and hence are both judge and party.
Three core functionalities that might be fundamental to quality and transparency control in clinical studies can be derived from the technical properties of Blockchain: (1) timestamping: this data-integrity property implies secure, consistent and incorruptible proof-of-data storage; (2) time-ordering: event consistency allows for checking the integrity of all timeordered events; and (3) smart-contracting: quality and security of automated checking allows for automatically building quality work-streams called Smart Contracts, or pieces of computer scripts executing contractual clauses on which co-contracting parties agree. Smart Contracts are pipelines of conditioned events that look like "IF Condition 1 AND Condition 2. AND Condition N (are met) THEN DO this ELSE DO that"  (Wang et al. , 2018).
io, 2019). Public Blockchain may also carry a cost in terms of security. Indeed, using a distributed network for clinical trials may seem counterintuitive because avoiding any breech of data privacy is mandatory in trials. However, Blockchain turns the openness of its network into an advantage: the lack of single node points of failure. It has been found quite resilient to security attacks and is constantly being improved  (Li et al. , in press). At a higher level, if Blockchain offers a wide range of technical tools that may consolidate the trustworthiness of clinical trials, these tools must be integrated with consistent and coherent governance. Hopefully, Blockchain technologies offer a way to design governance systems: public, permissioned or private Blockchains; open-or closed-source software and Smart Contracts; fixed or evaluative governance rules.
Examples are separating data storage and proof-of-data storage, customizable data-sharing and data request systems, and tokenization if some user incentivization is pertinent. The ultimate distributed governance culminates in the idea of Distributed Automated Organization, whereby equal control is granted to each participant  (Davidson et al. , 2016). However, this concept is not well-suited for our needs because constantly evolving governance may instill uncertainty in the stability of the processes. These governance models in the realm of health must be designed in close coordination with the legal context, which varies from state to state. For instance, compliance with general data protection regulations has strong implications, mainly (1) reidentification issues because a proof of data, even shaped as a hash, is considered potentially re-identifying data as long as the raw data from which the hash was derived is stored on a database.
In Europe, regarding storing proof-of-health data, some regulators in Europe recommend producing a risk analysis. Also, (2) the right to forget contradicts the append-only nature of the Blockchain data structure, from which no data can be overwritten. However, when the link between the hash and its raw data is deleted, then there is no issue for the hash to be stored as a persistent data, and since health data cannot not be stored beyond some time period depending on the state regulation, this problem becomes extinct with time (besides, such a data lifecycle could be taken care of by a Smart Contract). Importantly, standards for clinical trials remain to be consensually defined (e. g. , public or permissioned Blockchain, user control of implementation model, open source practices); this work is ongoing through Institute of Electrical and Electronics Engineers initiatives to standardize the technology https://blockchain.
We compute correlation function of Lyα absorption for the DR14 data in §3 and present our physical model for this function in §4. The results of fitting the data are presented and discussed in §5. We draw cosmological conclusions in §6 and summarize our results in §7. The computations of the correlation functions presented in this paper have been performed using a dedicated software package, Package for Igm Cosmological-Correlations Analyses (picca), developed by our team 2.
2017). This sample contains the first two years of eBOSS data and the five years of BOSS observations reprocessed using the eBOSS pipeline. It also includes data from the ancillary programs Time-Domain Spectroscopic Survey (TDSS) and SPectroscopic IDentification of ERosita Sources (SPIDERS). The quasar target selection is presented in  Myers et al. (2015). Note that eBOSS also targets quasars at low redshifts (where the Lyα region is not observable) to be used in other programs  (Ata et al. 2018; Wang et al. 2018; Blomqvist et al. 2018; Zhao et al. 2019; du Mas des Bourboux et al. 2019). The automated data reduction is organized in two steps  (Dawson et al. 2016). The pipeline initially extracts the twodimensional raw data into a one-dimensional flux-calibrated spectrum. During this procedure, the spectra are wavelength and flux calibrated and the individual exposures of one object are coadded into a rebinned spectrum with ∆ log(λ) = 10 -4.
Conclusions We have used Lyα and Lyβ spectral regions from the BOSS and eBOSS DR14 data sample to study BAO. Following B17, we have built a model for the Lyα auto-correlation function that we have then fit to the data. Our model incorporates the effects of redshift space distortions, the non-linear growth of matter, the contamination by metals and the modeling of high column density systems along lines-of-sight to quasars. Including UV fluctuations has only a minor impact on the fit results. We measure the ratios D H /r d and D M /r d at the average redshift of pixel pairs, z = 2. 34. We have also performed a measurement of these ratios from the Lyα auto-correlation function in two redshift bins, at z = 2. 19 and z = 2. 49. The D H /r d ratio is measured with a precision of ∼ 3. 3%, a slight improvement over the precision obtained by B17 for this ratio.
Acknowledgements. We thank  Pasquier Noterdaeme  for providing the DLA catalog for eBOSS DR14 quasar. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is http://www. sdss. org/. MB, MP and IPR were supported by the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR) , and by  ANR  under contract  ANR-14-ACHN-0021  SDSS is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatório Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
Real Example: To Explain or to Predict? Let us introduce now a real example well known in "the cause-effect pair community": the first cause-effect real pair of the Tübingen database. 2 Figure  3. 4 displays collected data on altitude (X-axis) and temperature (Y-axis) in the atmosphere from 349 meteorological stations in Germany over the years. T is the temperature and Z the corresponding altitude. We assume for this simple example that the observed dependency between T and Z is only due to a direct causal relation from one of the two variables to the other. We will see later in this chapter that it is not actually obvious and the model may be actually more complex, as at least one hidden variable latitude may also have an impact on both variables. In order to test causal hypotheses, the data are re-scaled with zero mean and unit variance, the dataset are split a large number of times between train (80%) and test sets (20%) and two alternative nonlinear Gaussian process regression models are learned on the train set.
First, the model B G , f,Q N with parameter θ from the parameter space Θ is send by the transmitter to the receiver (assertion). According to  [33]  the complexity of this model seen in term of total code length can be approximated as: C B (θ ) ≈-logπ(θ) + 1 2 log|J θ |- k 2 log(2π) + 1 2 log(kπ ), (3. 24) where |J θ | is the determinant of the Fisher information matrix defined as the matrix of expected second derivatives of the negative log-likelihood function with respect to all continuous parameters of the model, with entry (i, j ) given by: I(θ) i,j =- (x,y)∈R 2 f ((x, y)|θ) δ 2 δθ i,j log f ((x, y)|θ)dxdy. (3. 25) 2. Second, the data D are transmitted to the receiver using this model. The length of the detail, which encodes the data using the model defined by the set of parameter θ, corresponds to the negative log-likelihood of the data according to the model defined previously in Eq.
Evaluation In order to train this conditional GAN, a discriminative neural network d with parameter ω is used and the problem amounts to solve this min max optimization problem: S X→Y ( θ)=min θ max ω E x,y [log d ω (x, y)]+E x,n Y [log(1 -d ω (x, fY (x, n Y ; θ )))]. (3. 39) Then the principle used for causal discovery is to learn two CGANs: one with a generator fY from X to Y to synthesize the dataset D X→Y ={ (x i , ŷi )} n i=1 , and the other with a generator fX from Y to X to synthesize the dataset D Y →X = {( xi ,y i )} n i=1. Then, the causal direction is decided to be X → Y if the two-sample test statistic between the real sample D ={ (x i ,y i )} n i=1 and D X→Y is smaller than the one between D and D Y →X.
Datasets Five datasets with continuous variables are considered 6  : • CE-Cha: 300 continuous variable pairs from the cause-effect pair challenge  [11]  that will be presented with more details in the next chapter. Here we only consider pairs with label +1(X → Y ) and -1(Y → X) (notably the confounding case is excluded). • CE-Net: 300 artificial pairs generated with a neural network initialized with random weights and random distribution for the cause (exponential, gamma, lognormal, laplace. ). • CE-Gauss: 300 artificial pairs without confounder sampled with the generator of  [20] : Y := f Y (X, N Y ) and X := f X (N X ) Y := f Y (X) × N Y ), pre-additive noise (Y := f Y (X + N Y )) or pre-multiplicative noise (Y := f Y (X × N Y )). • CE-Tueb: 99 real-world cause-effect pairs from the Tuebingen cause-effect pairs dataset, version August 2016  [20].
This version of this dataset is taken from various domains: climate, census, medicine data. For all variable pairs, the size n of the data sample is set with a maximum of 1500 for the sake of an acceptable overall computational load. To provide an overview of the type of pairs encountered in each dataset, one hundred pairs of each of them are displayed in Fig. 3. 16 (the real pair with altitude and temperature given as introductory example corresponds to the first pair in the top left corner of CE-Tueb).
3. 5. 1. 1). • ANM: the Additive Noise Model  [20]  with Gaussian process regression and HSIC independence test of the residual (see Sect. 3. 5. 1. 2). • IGCI: the Information Geometric Causal Inference algorithm  [4]   For the methods Best mse and RECI we use the implementation provided in the CausalDiscoveryToolbox. 7  For the implementation of the algorithms ANM, IGCI, PNL, GPI and LiNGAM we use the R program available at https://github. com/ ssamot/causality. For CGNN we use the code available on github at https://github. com/GoudetOlivier/CGNN. We use default authors parameters for each algorithm implementation.
Performance Metric The task of orienting each pair observed pair as X → Y or Y → X is a binary classification problem. We propose to use two scores to evaluate the performance of the algorithms: •T h e accuracy score computed as the ratio of well oriented edges over the total number of edges for the datasets CE-Cha, CE-Net, CE-Gauss and CE-Multi. For the CE-Tueb dataset, a weighted accuracy is computed as in  [20]  in order to take into account dependent pairs from the same domain. •T h e area under the ROC curve computed using the causal score given by each model that measures the confidence of the approach in the causal orientation for each pair. This score is defined as the difference between the two scores of each candidate model evaluated in both directions (cf. Sect. 3. 4).
Results Table  3. 2 reports the area under the roc curve (AUROC) and the accuracy (in parenthesis) for each benchmark and each algorithm. The corresponding ROC curves are displayed in Fig. 3. 17. The method LiNGAM is outperformed as it uses linear mechanisms to model the data generative process which is in many cases unrealistic. IGCI does not seem to be very robust: it takes advantage of some specific features of the dataset, (e. g. the cause entropy being lower than the effect entropy in CE-Multi), but remains near chance level otherwise. The method ANM yields good results when the additive assumption holds (e. g. on CE-Gauss), but fails otherwise. PNL, less restrictive than ANM, yields overall good results compared to the former methods. The method RECI based on comparison of mean square error scores after proper re-scaling provides overall good results too.
However it fails for more complex pairs due to restrictive assumptions on the causal mechanisms involved. Lastly, methods like GPI and CGNN that admit the most flexible class of causal mechanisms and non-parametric metrics to match the distributions perform well on most datasets, including the realworld cause-effect pairs CE-Tueb, in counterpart for a higher computational cost (resp. 32 min on CPU for GPI and 24 min on GPU for CGNN). 8 et us note that better scores for the dataset Tübingen (CE-Tueb) are reported in recent papers such as  [16]  (accuracy of 82. 0%) and  [19]  (accuracy of 78. 7%) using ensemble methods with the algorithms presented in Sects. 3. 5. 2. 2 and 3. 5. 3. 3.
Need for Real Datasets of a Big Size Another important problem that is often overlooked in the "cause-effect pair community" concerns benchmarking. As of the writing of this chapter, the main real data benchmark used to compare the different methods by the community is the Tübingen Dataset, 9  composed of only 100 hundred pairs which are often very similar. It is indeed very difficult to collect cause-effect pairs with enough data points from the real world with an authenticated known ground truth. But one must to keep in mind that it is very easy for most of the methods presented in this chapter to tune their hyper parameters (even unintentionally) in order to obtain the best results. This overfitting problem is often compounded by the fact that this dataset is, most of the time, not separated into train/validation/test sets.
It has led us to propose a reading grid of the main methods proposed in the literature. Three main families have emerged:  (1)  methods that restrict the class of admissible causal mechanisms and focus on identifiability results; (2) methods that do not explicitly restrict the class of admissible mechanisms and focus on the generality of the approach at the expense of theoretical identifiability guaranties; and (3) lastly methods that exploit the postulate that the cause should be independent of the causal mechanism. We have then compared the main methods of the literature whose code is available online. It appears that methods that allow for flexible causal mechanism and complex realistic interactions can obtain consistent scores on a wide range of cause-effect problems (artificial and real data).