dataesr/scientific-paragraphs-categorization huggingface dataset:
Roughly, we divide those who interact with scientific software or analysis code in two categories. First, the authors ("A") who propose a method and need to verify its validity and usefulness with public and/or their own -often private -data. Second, the users ("U", e. g. developers, engineers, bioinformaticians) who need to test and evaluate the proposed methods with other data. When authors launch a research about a method, this method must belong to a general category of methods (e. g. classification, regression) and could have a reproducibility profile, which will progressively be built by authors and users (  Fig 4 b. 3, b. 4). Even the information of which method does or does not work with a standard data is a crucial information for future work. During optimization of project, the programming code with guide should be accessible although authors do not publish.
Conclusion and perspective Across the scientific fields, the reproducibility issue is seen as a growing concern. Before reusing a published method, we attempted to reproduce the initial results and recoded the method to have a deep understanding of it. The investment in time to verify a previously published method can be more important than the work needed to publish a new paper. Despite the willingness of the authors to share their tool and help us in our work, we have faced computational reproducibility and robustness problems due to compatibility between environments, programming languages and software versions, choice of parameters, etc. In addition to individual effort to write well documented and readable code, we recommend to use online repositories and tools to help other scientists in their exploration of the method: Docker for environment standardization, GitHub for code version management, and Jupyter notebooks for demonstration and tutorial  [20, 21, 24].
Scientists are strongly encouraged to adopt such practices, not only for writing code but also manuscripts  [4]. At the community level, we should enhance the cooperation between academic education and industry to foster a new generation of well-trained scientists in software development. For instance, Academia-Industry Software Quality & Testing summit (AISTQ) has organised conference in order to encourage collaboration between Academia and Industry  [38]. Here, we propose a workflow system where the community uses standard datasets to validate tools. The proposed method success on data profile will be evaluated continuously with new datasets. Eventually, data and software can be versioned and cited to give credit to the individuals who have contributed to these building blocks of Science.
This workflow is not merely a reproducibility validation tool, it is an attempt to make research product more reusable by the community using an online platform, beyond the publication process. Such system could be seen as a generalisation of already existing workflow systems such as Galaxy or GATK, integrating data provenance  [39, 40]. Some top-down initiatives already provide some incentives for such a process i. e. Horizon 2020 (H2020) [41] project of the European Commission (EC) mandates open access of research data, while respecting security and liability. H2020 supports OpenAIRE  [42] , a technical infrastructure of the open access, which allows the interconnection between projects, publications, datasets, and author information across Europe. Thanks to common guidelines, OpenAIRE interoperates with other web-based generalist scientific data repositories such as Zenodo, hosted by CERN, which allows combining data and GitHub repository using DOI.
Funding This work was supported by the  Institut Pasteur ;  Centre National de la Recherche Scientifique ;  University Paris Diderot ; the  Conny-Maeva Charitable Foundation ; the  Cognacq-Jay Foundation ; the  Orange Foundation ;  Fondation pour la Recherche Médicale ; the  GenMed Labex ; and the  BioPsy Labex. The research leading to these results has received funding from the  H2020 research program   COSYN.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the  Agence Nationale de la Recherche  ( ANR-11-JSH2-003-1 ), the  Mission pour l'Interdisciplinarite´(Defisens, Supple´-Sens  grant), and the  Fondation des Aveugles de Guerre.
The Data Warehouse (DW) is characterized by complex architecture, specific modeling and design approaches. It integrates data issued from operational data sources in order to meet decision-makers' needs by providing answers for OLAP queries (On-Line Analytical Processing). In practice, both data source models and decision-makers' analytical requirements evolve over time and, therefore, lead to changes in the DW multidimensional model. In this evolving context, we have developed the DWE (Data Warehouse Evolution) framework. DWE automatically propagates the changes of the data source data-model on the DW data-model. This paper proposes a model-driven approach for extending DWE in order to consider a further related evolutionary aspect: The evolution of decision-makers' needs. It deals with the propagation of these evolutions on the DW multidimensional model.
These changes are due to two main reasons: (a) Evolution of analytical needs of decision-makers: changes in these needs might require extending the DW model (e. g. adding new axes or subject of analysis), and (b) Evolution of data source model (DS) dictated by the evolution of the organization business processes (e. g. , adding or even deleting conceptual entities). To the best of our knowledge, we claim that the problem of changes in the DW model has not been sufficiently addressed yet neither by the research community nor by the DW software editors. As well, all evolution strategies of the literature are at a single modeling level: schemas before and after changes are conform to the same meta-model. To the best of our knowledge, in the DW domain, the evolution of schemas expressed in different models have not yet received their full part of investigation.
To alleviate this problem, we proposed in  (Taktak et al. , 2015)  an MDA (Model Driven Architecture) approach that automates the propagation of the evolution of the DS model towards its associated DW model. In this paper, we extend our contribution by considering the impacts of changes of decision-makers' requirements on the DW data-model (DW). In this context, we suggest an approach based on a classification of evolution scenarios and a set of transformation rules for the identification of evolution operations to apply on the DW model. This paper is organized as follows. In Section 2, we give a review of works dealing with the DW evolution problem. Section 3 describes our MDAbased approach for the propagation of decisional requirements evolution towards the multidimensional DW model; this approach extends our framework DWE «Data Warehouse Evolution».
Approaches based on DS Evolution Model Organizations' business processes evolve over time. This is due to the modification of existing processes or to the emergence of new processes with new real world objects. These evolutions affect the datamodel of the information system that feeds the DW. In turn, the DW cannot be immunized against these evolutions in its data source; consequently this evolution deserves to be studied so that it becomes semi-(automatically) propagated towards the DW data-model, the DW ETL (Extract Transformed and Load) process, and stored data. This evolution problem was addressed from different viewpoints. We can classify the related works into three main categories: (i) Evolution of the DW multidimensional model, (ii) Maintenance of materialized views, and (iii) Adaptation of the ETL process.
However, this study was restricted to the ETL process without treating the impact of the DS evolution on the DW model (Dimensions, facts, hierarchies …). To address these shortcomings, the authors of  (Wrembel and Bebel, 2007 ) defined a formal model for a multi-version DW. They presented a set of evolution operations that affect the DW schema and its instances. These authors have distinguished two types of DW versions: real version and alternative version. The DW real version reflects the changes in the real world environment of the organization whereas the DW alternative version simulates the change process; it bases on "What-If" analyses. In order to validate their approach, they developed the MVDW (Multi-Version Data Warehouse) prototype for the maintenance of the DW and the management of its versions.
Moreover, note that the proposed changes are simple: they do not cover all cases that decisionmakers may ask for. To surmount this problem, Talwar and Gosain (2012) studied the evolution of complex hierarchies (multiple alternative hierarchies, dependent and independent parallel hierarchies). They defined a set of evolution operations equipped with constraints to ensure data integrity and schema consistency of the new DW model. Operations and constraints are defined in ULD (Uni-Level Description language) and MDD (Multilevel Dictionary Definition). This study is an extension of the work of  (Thakur and Gosain, 2011)  where the authors presented a conceptual requirement-oriented framework called DWEVOLVE for DW evolution. It analyzes the changes in the requirements specified by stakeholders as well as developers, and then incorporates them into the DW by performing appropriate additions, deletions and updates.
Derivation by Rule For this derivation type, simply we need to define rule(s) to apply on an element of the DW model in order to derive a new element to integrate into the same DW model. The general derivation form of this could be of the following form: n = (a) where n is the derived attribute,  is a user-defined extraction function, and a is a DW attribute. The derivation by rule is convenient for complex codes of entities (i. e. , identifiers composed of small pieces of information), as the French civil identifier INSEE code (2-digits department code + 3-digits city code), or a product code including the product category and sub-category or even the country of origin. We can easily derive (extract) new data from these codes. In addition, a Date attribute likely to generate the day, month, quarter… for a Time dimension.
As an illustrative example, consider the DW in Figure  2 , and let us assume that a decision-maker chooses to analyze the sales by Category of products. The criterion of analysis Category (i. e. , hierarchic parameter) is not currently present in the multidimensional model despite it exists implicitly in the data source. Nevertheless, the decision-maker knows that the last digit of the product identifier (Id_Prod) indicates the product category. The derivation of the category exploits this knowledge as an extraction rule. But, how to deal with the new extracted data called Category? What is the role of this Category in the DW model (is it a dimension, parameter within a new or an existing hierarchy?). Here the insertion of the Category creates a new hierarchy for the D_PRODUCT dimension (cf.
Evolution by Extension The third class of Evolution requires extending the DW model by new elements extracted from its associated DS. These evolutions apply when the current DW model is inadequate to meet the new requirement either by derivation or by reformulation. In this case, it is necessary to identify which element from the DS we should add to the DW. For example, the decision-maker needs to analyzing sales according to the products' providers. This data is currently absent in the DW but the Provider table exists in the DS. Besides, we have a Foreign Key constraint from Product to Provider indicating the existence of a functional dependency associating each product to its provider. PRODUCT feeds the dimension D_PRODUCT of the F_SALE fact then Provider becomes a hierarchic level (parameter) in the D_PRODUCT dimension.
CONCLUSION In this paper, we have addressed the problem of evolution in the decision support systems. In particular, we have studied the effect of the evolution of decision-makers' requirements on the multidimensional DW model. To do so, we have proposed an extension of our DWE (DW Evolution) prototype that addresses evolutions coming from data sources as well as evolutions due to the new requirements of decision-makers. We have elaborated a classification of scenarios of possible evolutions namely reorganization, extension or diversion. Furthermore, for each of these evolution classes, we have proposed a process for identifying DW evolution operations (add fact, add level. ). Our approach takes advantage of the Model-To-Text transformations implanted in our DWE, which we reuse to transform these new evolution operations into OMB executable scripts.
• Processing: this layer consumes the streaming data buffered by the ingestion layer and sends the output and intermediate results to the storage or ingestion layers. In practice, each layer is implemented as a dedicated solution that is independent from the other layers, under the assumption that specialization for a particular role enables better optimization opportunities. Such an architecture can be found in  [8]  where the data collection and stream database (storage) are handled separately. However, as the need for more complex online data manipulations arises, so does the need to enable better coupling between these three layers. Emerging scenarios (Section IV) emphasize complex data pre-processing, tight fault tolerance and archival of streaming data for deeper analytics based on batch processing.
Support for Search. The search capability is a big challenge: this is more appropriate to specialized storage systems. Ingestion frameworks usually do not support such feature. A large number of stream applications  [17]  need APIs able to scan (random or sequential) the stream datasets and support to execute real-time queries. Advanced Support for Message Routing. Routing defines the flow of a message (record) through a system in order to ensure it is processed and eventually stored. Applications may need to define necessary dynamic routing schemes or can rely on predefined static routing allocations. Readers can refer to  [18]  for a characterization of message transport and routing. Backpressure Support. Backpressure refers to the situation where a system cannot sustain any more the rate at which the stream data is received.
A. Overview Apache Kafka  [22]  is a distributed stream platform that provides durability and publish/subscribe functionality for data streams (making streaming data available to multiple consumers), being the de facto open-source solution (for temporal data persistence and availability) used in end-to-end pipelines with streaming engines. Redis  [23]  is an in-memory data structure store that is used as a database, cache and message broker. Redis also implements the pub/sub messaging paradigm and groups messages into channels with subscribers expressing interest into one or more channels. Hyperion  [24]  is a system for archiving, indexing, and online retrieval of high-volume packet header data streams. Data archiving for network monitoring systems is complicated by data arrival rates and the need for online indexing of this data to support retrospective queries.
The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets.
The composite-likelihood score can be used as a criterion to estimate the parameters of the model with faster algorithms than the coalescent samplers although with lower performance, particularly regarding confidence intervals (e. g. ,  Navascués, Hardy & Burgarella, 2009; Nikolic & Chevalet, 2014). Coalescent models can also be used in the likelihood-free framework known as Approximate Bayesian Computation (ABC,  Tavaré et al. , 1997; Beaumont, Zhang & Balding, 2002). In this approach, the likelihood is substituted by the similarity between the observed data and simulated data generated from a given model. Similarity is usually evaluated by means of a distance between observed and simulated summary statistics. This distance allows one to select the simulations close to the observed data and reject those too far away.
Skyline plots were introduced by  Pybus, Rambaut & Harvey (2000)  who estimated the effective population size in the time intervals defined by the coalescent events of a given genealogy (which was considered as known) from the expected waiting time between coalescent events. The graphical representation of those estimates suggests the skyline of a city, giving the name to the method. Such models have been implemented in a Markov chain Monte Carlo coalescent sampler (BEAST software;  Drummond et al. , 2005; Minin, Bloomquist & Suchard, 2008; Heled & Drummond, 2008) , and in an importance sampling coalescent sampler  (Ait Kaci Azzou, Larribe & Froda, 2015)  for the analysis of sequence data. The addition of microsatellite mutation models to BEAST (Wu, Drummond & Uyenoyama 2011) made it possible to infer skyline plots from this type of data (e.
g. ,  Allen et al. , 2012; Molfetti et al. , 2013; Minhós et al. , 2016). Also for microsatellite data, a composite-likelihood approach has been developed (R package VarEff,  Nikolic & Chevalet, 2014). Similar piecewise models to infer historical population sizes through time have been proposed in the context of population genomics (e. g. ,  Li & Durbin, 2011; Terhorst, Kamm & Song, 2016). The methods discussed above assume a set of independent (unlinked) genetic markers. However, if a large proportion of the genome has been sequenced, the studied polymorphism are in linkage disequilibrium. Methods such as the Pairwise Sequentially Markovian Coalescent (PSMC,  Li & Durbin, 2011)  and its successors profit from the additional information of linkage disequilibrium for the inference. We will not further discuss this family of methods, as our focus here is on datasets of independent molecular markers, such as microsatellites, which remain reliable markers for low-budget projects.
Note, however, the PSMC-like implementation on ABC by  Boitard et al. (2016). The use of the skyline plot in the ABC framework was first proposed in  Burgarella et al. (2012). Here, we provide a suite of R scripts (DIYABCskylineplot) to produce approximate-Bayesian-computation skyline plots from microsatellite data and evaluate its performance on simulated pseudo-data. We show the method to be useful for detecting population decline and expansion and discuss its limits. ABC skyline plots are then built for four study cases (whale shark, leatherback turtle, Western black-and-white colobus and Temminck's red colobus) and compared with the demographic inference obtained by an alternative full likelihood method.
We implemented this approach in a suite of R scripts (R Core Team, 2017) that we named DIYABCskylineplot  (Navascués, 2017). For each simulation the number of population size changes is sampled using the prior probabilities. Through a command line version of DIYABC (v2. 0,  Cornuet et al. , 2014) , parameter values {(θ k,i ,τ k,i );i ∈ [0,n k ]} are sampled from the prior distribution, coalescent simulations are performed and summary statistics are calculated (mean across loci of the number of alleles, N a ; heterozygosity, H e ; variance of allele size, V a , and  Garza & Williamson (2001)  statistic, M ). In addition, the Bottleneck statistic ( H ;  Cornuet & Luikart, 1996) , which compares the expected heterozygosity given the allele frequencies with the expected heterozygosity given the observed number of alleles, is calculated in R from the summary statistics provided by DIYABC.
Data sets In addition to PODs, four real data-sets from the literature were re-analysed with the ABC skyline plot described above. The first data-set comes from the whale shark (Rhincodon typus), the largest extant fish. Whale sharks inhabit all tropical and warm temperate seas and are considered an endangered species with a global population decline of more than 50% in the last three generations  (Pierce & Norman, 2016). We have re-analysed a data-set of 478 individuals genotyped at 14 microsatellite loci from  Vignaud et al. (2014). The second example is the leatherback turtle (Dermochelys coriacea), the most widely distributed sea turtle found from tropical to sub-polar waters. The global population has been reduced in about 40% in the last three generations. As species, the leatherback turtle is classified as vulnerable, mainly because of the Northwest Atlantic population that shows an increase in number of nests.
However, other populations are critically endangered  (Wallace, Tiwari & Girondot, 2013). The data-set re-analysed (215 individuals genotyped at 10 mocrosatellite loci;  Molfetti et al. , 2013)  comes from the Northwest Atlantic population. Last, we re-analysed the data from the populations of two colobus monkeys at the Cantanhez National Park in Guinea Bissau  (Minhós et al. , 2016). The Western black-and-white colobus (Colobus polykomos, 22 individuals genotyped at 14 loci) and the Temminck's red colobus (Procolobus badius ssp. temminckii, 23 individuals genotyped at 13 loci) are two sympatric species from the Western African rainforest considered to be vulnerable and endangered respectively  (Oates, Gippoliti & Groves, 2008; Galat-Luong et al. , 2016). Data were analysed with the same prior distributions as PODs except for the colobus monkeys datasets, which consist of tetranucleotide markers.
Previous evidence suggests that tetranucleotide microsatellite mutations are mainly of only one repeat unit (e. g. ,  Leopoldino & Pena, 2003; Sun et al. , 2012). In order to incorporate this prior knowledge, half of the simulations had P GSM = 0 (i. e. , a strict stepwise mutation model, SMM) and the other half had the parameter sampled from a uniform distribution in the range (0, 1). For comparison, demographic history of the four real data sets was also explored using the MIGRAINE software  (Rousset & Leblois, 2016 , http://kimura. univ-montp2. fr/~rousset/ Migraine. htm ) under the model of a single panmictic population with an exponential change in population size. To infer model parameters, MIGRAINE uses coalescence-based importance sampling algorithms under a maximum likelihood framework  (Leblois et al.
Regarding the mutational model, a large proportion of multi-step mutations seems to be present in all datasets, with P GSM estimates: PGSM = 0. 55 (95% HPD = 0. 46-0. 62) for the whale shark; PGSM = 0. 50 (95% HPD = 0. 38-0. 60) for the leatherback turtle; PGSM = 0. 43 (95% HPD = 4. 05 ×10 -3 -0. 53) for the black-and-white colobus; and PGSM = 0. 18 (95% HPD = 0. 02-0. 75) for the red colobus (see also Fig. S16 ). Although very small values of P GSM are included in the credibility interval from the colobus analyses, the GSM is favoured over the SMM when an ABC model choice analysis is performed (BF = 57. 50 for the black-and-white colobus and BF = 10. 01 for the red colobus). These results are congruent with estimates of P GSM by MIGRAINE (Table  S3 ).