dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar initiatives have been launched in the USA [8] Pancancer Analysis of An international collaboration to identify Hybrid cloud model. The data-coordinating [67, 68] Whole Genomes (PCAWG) common patterns of mutation in more than center lists collaborative agreements with 2800 cancer whole genomes from the cloud providers' AWS and an academic com- International Cancer Genome Consortium. This puting cloud resource maintained at the can- project is exploring the nature and cer collaboratory, by the Ontario Institute for consequences of somatic and germline Cancer Research and hosted at the Compute variations in both coding and non-coding re- Canada facility gions, with specific emphasis on cis-regulatory sites, non-coding RNAs, and large-scale struc- tural alterations RD-Connect RD-Connect is an integrated platform Online secured platform connecting different [69] connecting databases, patient registry data, types of patient-related rare disease data, en- biobanks, and clinical bioinformatics for rare abling genome-phenome analysis disease research.
We classify these trust types into two main levels  (Scott 2005) , referring to them as micro-level trust (i. e. , retailer-and personnel-trust) and macro-level trust (i. e. , country-trust), and examine how trusts at different levels influence consumers' willingness to share their personal information (WSPI) with retailers. To ensure variance in the country-trust, we collected data in fourteen countries: Argentina, Australia, Brazil, Canada, China, Colombia, France, Italy, Japan, Mexico, South Africa, Spain, the UK, and the US. Second, the type of product that retailers sell may condition customers' disclosure propensity; our study therefore considers seven different types of product categories that retailers sell: men and women apparel, children and teenager apparel, luxury goods, pharmaceuticals, grocery, home décor and DIY goods, and consumer electronics.
In the research design phase, we therefore guaranteed the survey respondents' anonymity and their data's confidentiality. We also structured the questionnaire to maximize the psychological and methodological separation of the questions referring to our model's variables. We added a few differently structured questions to map the overall shopping decision process, mentioning that this was a market research survey to mask the study's real objective and to avoid desirability bias  (Nederhof 1985). Finally, CMB was assessed in our data by means of Harman's single-factor test  (Podsakoff et al. 2003)  and the marker variable technique  (Lindell and Whitney 2001) , whose results are reported in web appendix C and show that CMB is not an issue in our database. The same web appendix reports on our multicollinearity tests, which were once again not an issue.
Post-hoc analysis on trust effect size across contexts The last aim of our analysis was to see if a taxonomy of retail contexts, based on trust's different effect sizes, could be identified  (Wang et al. 2017). To this end, we ran the level 1 model on each context in our database (Eq. (  1 )). A context was identified by combining countries, product categories, and information types in our sample. This resulted in the analysis of 686 contexts in our study. The next step consisted of employing k-means cluster analysis  (Hair et al. 1998; Milligan 1980). Table  5  illustrates the effect sizes' cluster means, which was the final assignment of cases into clusters, resulting in three clusters of n 1 = 248 (36% of the sample), n 2 = 235 (34% of the sample), and n 3 = 203 (30% of the sample). The ANOVA indicates significant mean differences across the three clusters in respect of all the effect sizes, with the exception of the interaction between PCs and retailer-trust.
Acknowledgments The data collection was conducted with the support of the  Institute of Business Value (IBV)  of  IBM  within its collaboration with the  SDA Bocconi school of Management Professors. The analysis for this paper was carried out using the  STEVIN Supercomputer Infrastructure at Ghent University , funded by  Ghent University , the  Flemish Supercomputer Center (VSC) , the  Hercules Foundation  and the  Flemish Government -department EWI.
Data Sources: American Life Panel Survey and CRSP We fielded our survey module in the RAND American Life Panel 10 from June 20 to July 19, 2017. The ALP includes several thousand households that regularly answer internet surveys. To 9 Although widely used in the finance literature, the  Tversky and Kahneman (1992)  function generates an artificial negative correlation between the utility curvature and the probability weighting parameters  (Fehr-Duda and Epper, 2012). Thus, for this function we do not jointly estimate the utility curvature and probability weighting parameters. 10 Online Appendix G and https://www. rand. org/labor/alp. html provide further information on the ALP. limit selection bias, households lacking internet access at the recruiting stage are provided with a laptop and wireless service.
To ensure that the sample is representative of the U. S. population and of equity investors in the U. S. , we use survey weights provided by the ALP for all analyses and summary statistics reported in this paper. In addition to the probability weighting variables, our module also collects information on portfolio choice and some control variables. We obtain other controls such as demographic and economic characteristics from earlier survey modules. The ALP invited 3,397 panel members and closed the survey when 2,703 of them completed the survey, a completion rate of 79. 5%. 11 Our data are cross-sectional, as the ALP does not include a regular time-series on investments and wealth for all subjects at regular intervals. Respondents who indicate that they hold individual stocks are asked to list the names (or tickers) of their five largest holdings.
We match the holdings to the CRSP database 12  and construct various measures of stock characteristics using daily returns from July 1, 2016 to June 30, 2017. Table  2  provides summary statistics of the key variables, both for the full sample and for the 741 respondents who own equity outside of retirement accounts (Appendix Table  A1  defines the variables). Our main analyses focus on the sample of 741 equity holders. We examine only equity investments outside of retirement accounts, because retirement investments may not reflect active choices due to limited investment options and the Department of Labor's acceptance of target date funds as investment defaults. 13 Few 401(k) plans allow investment in brokerage accounts and only a small fraction of pension assets is invested via these accounts  (Keim and Mitchell, 2018; Vanguard, 2019).
As described in Section 2. 2, we match these stocks to the CRSP daily stock return database and construct various measures of skewness: Total Skewness, Idiosyncratic Skewness, Idiosyncratic σ, and Max. One-Day Return. Although people may not understand the statistical concept of "skewness," they likely do understand which stocks have more lottery-like features -those with higher skewness. We include Idiosyncratic σ because it is a proxy for expected positive skewness  (Boyer, Mitton, and Vorkink, 2010) , not because probability weighting implies a preference for idiosyncratic risk itself. We also include the market beta, Stock β, as a measure of systematic risk. Table  8  shows regression estimates for the five dependent variables described above. The key independent variable is Inverse-S.
Details are in Online Appendix F. All models include a constant, missing data dummies, and controls for female, 5-year age category dummies, married, white, Hispanic, number of household members, employment status, education, (ln) family income, (ln) financial wealth, (ln) housing wealth, mortgage-to-value ratio, numeracy, financial literacy, experienced stock returns, trust, utility curvature, and optimism. This table reports the coefficients of OLS regressions. The key independent variable is Inverse-S. The dependent variables are generated using the characteristics of the stocks held by the subjects, and they are calculated using daily returns over the period July 1, 2016 to June 30, 2017. In Panel A, the analyses are at the stock level and standard errors are clustered at the respondent level.
All models include a constant, missing data dummies, and controls for female, 5-year age category dummies, married, white, Hispanic, number of household members, employment status, education, (ln) family income, (ln) financial wealth, (ln) housing wealth, mortgage-to-value ratio, numeracy, financial literacy, experienced stock returns, trust, utility curvature, and optimism. This figures shows the annualized standard deviation and skewness as a function of the number of stocks. To create the figure, we randomly select N stocks from the set of CRSP domestic common stocks to form an equally weighted portfolio. We then find the (annualized) standard deviation and skewness of this equally weighted portfolio using daily returns over the period July 2016 to June 2017. We repeat this procedure 1,000 times and plot the average portfolio statistics for each N.
Introduction Data warehouses (DWs) are databases specifically aimed at supporting decision makers in extracting and analyzing useful information from heterogeneous data sources, and they are widely used within both the academic and industry communities. More precisely, a DW is a subject-oriented, integrated, time-variant and non-volatile collection of data to support the decision-making process  [29]. The data in a DW are organized in the form of cubes structured according to the multidimensional model. A cube is focused on a phenomenon of interest called fact (e. g. , sales or invoices). The occurrences of a fact are called events; they are described by numerical measures (e. g. , the quantity sold or the invoiced amount) and can be analyzed along dimensions (e. g. , data, product and customer).
Case study The methodology we propose will be explained using a case study concerning the analysis of biodiversity data in the agricultural context, related to the French ANR project VGI4Bio. The VGI4Bio project aims at collecting crowdsourced data from farmers about the presence of some particular taxa (butterflies, worms, etc. ) in their plots  [5]. These data are integrated with the agronomic characterization of each plot, e. g. , its crops and the usage of pesticides. The decision makers involved in the project belong to different domains and organizations: They are farmers, public managers, ecologists, etc. They have different skills and analysis goals, but all of them are non-skilled in OLAP and DWs. An example of a multidimensional schema used in VGI4Bio is shown in Fig. 2  using the ICSOLAP UML Profile  [7].
For instance, data may be missing for some level or two source tables may not have an explicit relationship to build a well-defined hierarchy  [5]. An early prototyping phase  [3]  and a method to validate the prototype on data sources  [17]  are very beneficial in this setting to confirm the feasibility of the implementation before the costs for error correction become too high. Other key factors in reducing the time needed to converge to a proper multidimensional schema by reducing the number of errors are recognized to be a strong commitment of decision makers in the project and the adoption of an iterative methodology  [19]. Finally, taking into account quality metrics in database and DW design has been investigated in the literature to some extent  [52] ; specifically, metrics related to DW usability  [18] , accessibility, correctness, etc.
Goal modeling This step covers elicitation, specification and validation of requirements. These activities are carried out informally in some methodologies, for instance using interviews, questionnaires, workshops, etc. In other cases, they are carried out more formally, using for instance use case modeling or goal definition techniques as in  [16]. Indeed, it is commonly agreed in the literature that goal definition plays an essential role in requirements-driven design methodologies to accurately capture users' requirements (see  [23]  for an extensive survey of the recent literature on goal-oriented requirements engineering). For this reason, to handle this step we adopt the software development methodology Tropos  [8] , which has been successfully adapted to DW design in GRAnD  [16]. The main concepts of GRAnD are actors, which represent decision makers, and rationale diagrams, which represent the actor's goals, their dependencies with other actors and their AND/OR decompositions into subgoals.
The main reason for using e-pivot tables is actually to identify dimensions and measures in an example-driven way, without requiring a deep understanding of multidimensional modeling from decision makers. Specifically, dimensions and measures will be determined starting from the headers of each e-pivot table. The goals and subgoals of each decision maker are iteratively defined by the DW expert and then validated by the decision maker herself, until a consensus is reached. Figure  9  shows, concerning our case study, the goal model for two decision makers. The model includes two main goals: "Biodiversity monitoring," from "Farmers and Ecologists" actors and "VGI monitoring," from "VGI Database managers" actors. The former is used to describe the ecological biodiversity phenomenon (for instance, using average abundance as an indicator).
The role of DW experts during this phase is not only that of passively tracing the requirements expressed by decision makers in the form of e-pivot tables, but also that of actively guiding decision makers in producing good-quality requirements. In this direction, the specification of each e-pivot table is followed by a semi-structured interview whose questions are specifically aimed at addressing quality issues related to the multidimensional schema to be obtained. Similarly to  [52] , which provides some quality metrics (accuracy, consistency, completeness and timeliness) over databases and decisional queries, we introduce some quality features for DW schemata as summarized below: • Completeness: each hierarchy has all the necessary levels; the multidimensional schema has all the necessary dimensions and indicators.
Multidimensional modeling The goal of this phase is to translate each e-pivot table into a multidimensional schema and merge the resulting schemata, which may be related to the core phase of query-driven approaches. In  [38] , some a priori knowledge of the user requirements is assumed to be available, though no original techniques are proposed to build it, and the multidimensional schema is obtained by matching this knowledge against the query graphs provided by users. In our setting, this approach cannot be applied, since there is no a priori knowledge-e-pivot tables are the only source of requirements. In  [45] , users express queries in SQL over the source (relational) database; however, the algorithm that creates the multidimensional schema starting from these queries also uses information coded within the source database, so it cannot be reused in our setting because data sources are not known in advance.
Implementation Several approaches have been proposed for mapping requirements over data sources (e. g. ,  [12] ). In particular,  [6]  and  [34]  provide methods to validate conceptual multidimensional model over relational data sources, but they do not take into account irregular schemata as previously described. Indeed, the existing work assumes that the DW schema meets the multidimensional normal forms  [35] , which exclude irregular structures and assume data summarizability. Moreover, in the existing work data sources are typically represented as relational databases, since the mapping algorithms operate on primary and foreign keys. This is another important limitation, since data lakes, where different types of data and storage systems (relational, textual, NoSQL, etc. ) are joint together, are increasingly present in companies.
Due to the inherent complexity of devising an automated approach for the validation of (irregular) multidimensional schemata over (heterogeneous) data sources, in this work we advocate for a manual mapping, to be performed by DW experts in collaboration with decision makers and databases administrators. When a multidimensional element cannot be found in data sources, it is removed by the DW schema, and the e-pivot table involved is sent back to the decision maker to be updated. This kind of situation was quite infrequent in  our context, as decision makers have good knowledge of the data sources  [5]. Prototyping is one of the most used requirements elicitation and validation methods in the development of both DWs and generic software. In our methodology, once an ICSOLAP model has been obtained at the previous step, it is implemented by DW experts using the ProtOLAP tool  [3].
In this way, decision makers can access their pivot tables on the OLAP client and use them to validate their requirements. ProtOLAP takes in input a UML model defined with ICSOLAP through the MagicDraw CASE tool and automatically creates the SQL scripts for Postgres (table creation and data insertion) as well as the XML configuration files for the Mondrian OLAP server. Importantly, this step enables the quick creation of a prototype to be shown to decision makers on the one hand; on the other, it allows DW experts to check for the feasibility of implementing the DW schema on the OLAP server and the DBMS. Before the ICSOLAP schema can be fed into ProtOLAP and prototyped, it may have to be transformed, because irregular multidimensional schemata are not natively supported by Mondrian. Some solutions have been proposed in the literature to transform irregular multidimensional schemata; these solutions have pros and cons, and the choice of the best one depends on the application domain and decision makers' needs.
Data warehouses and unstructured data Since a couple of decades ago, there has been an increasing interest in incorporating unstructured data into the decisionmaking process. The first attempts in this direction were essentially aimed at designing a DW in a data-driven fashion starting from unstructured (typically, XML) data rather than relational databases, e. g. ,  [54]. These approaches are often classified as schema-on-write, as they force unstructured data to fit a structured and fixed schema at the time of loading them into the DW. A more recent trend is that of storing unstructured data in a so-called data lake rather than in a DW, leaving them in their raw format; then, data can be analyzed using a schema-on-read approach, i. e. , adapting them to some (flexible and user-defined) schema only at the time of accessing them.
Data sources Data were collected from the set of all Cochrane reviews published or updated between March 2011 and September 2014. The same set was used for a previous study  [17]. The process of selection, evaluation of agreement, and indepth analysis of disagreements was described in detail elsewhere  [17]. Briefly, for each review, we obtained all data entered by review authors in RevMan, the software used for managing Cochrane reviews  [11] , as xml files  [18]. We identified RCTs assessed for risk of bias in two separate systematic reviews. When more than two different reviews included the same study, we randomly selected one of the possible pairs. For each of the five key risk-of-bias items (random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, and incomplete outcome data), we compared the risk-of-bias judgments between the two reviews to evaluate whether they agreed (e.
Data extraction We collected the following information regarding characteristics of reviews and studies included in the review: Data regarding the review: Year of publication of the review. Data regarding the RCT in comparison to the other RCTs included in the review: Sample size of the RCT in comparison with the other studies included in the review: From the xml file containing all the information reported in the review, we extracted the sample size of all studies included in the review. We calculated the distribution of the sample size for the RCTs included and then calculated the percentile of the sample size for the RCT of interest, expressed in a decimal number from 0 to 1. Year of publication of the RCT in comparison with the other studies included in the review: We followed the same process explained previously for the year of publication of the RCTs included in the review, calculating the rank percentile for the RCT of interest.
Statistical analysis We evaluated characteristics potentially associated with judgment of high vs. unclear risk of bias and low vs. unclear risk of bias by using a conditional logistic regression model, accounting for the matched design. We evaluated each factor separately in a univariate analysis. The factors analyzed derive from the data we extracted and are listed in the previous paragraph. The results are expressed as odds ratios (ORs) with 95% confidence intervals (CIs) for each factor, considering separately disagreements of an assessment of high vs. unclear risk of bias and low vs. unclear risk of bias. Quantitative variables were analyzed as such, without dichotomization. We conducted a single joint analysis across different items and then subgroup analyses for each item separately.
We used the number of reviews conducted by the review group as a measure of the volume/experience of the Cochrane review group. Authors: We collected the name of the first author and searched the number of Cochrane reviews conducted by this author up to the end of the period of interest (September 2014). The first reviews included in the Cochrane Database of Systematic Reviews date from 1995; therefore, we have identified all Cochrane reviews from 1995 to 2014. We also evaluated whether authors of the review were also authors of included RCTs (shared authorship RCT/review). Size of the review (number of RCTs included in the review). Type of interventions analyzed in the review (only pharmacological/only nonpharmacological/both). Mention of any type of conflict of interest by review authors.
Outcome of the future users and data providers consultation 1. Building of a controlled vocabulary (variables names and categories) 2. Definition of the required metadata: analysis of standards (ISO19115, INSPIRE, DataCite, O&M, etc. ) to define the information flux to be organized between observatories and the central Theia/OZCAR IS 3. Definition of a pivot data model for exchanging the information https://github. com/theia-ozcar-is/data-model-documentation 4. Definition of the architecture of the Information System and building of a prototype web portal What has been done so far for data discovery? https://in-situ. theia-land. fr/skosmos/en/ Global change / Critical zone • Network : 21 long term in situ observatories • Open data • Thematic data pole (continental surface) • 4 Thematic data pole (5?) A common information system : Theia/OZCAR Fundings : INSU, IRD, IR OZCAR, ANR FairTOIS 25 « European Long Term Ecosystem Research » RI on the European road map since 2018.
Acknowledgements: The authors thank all the participants of the " Tour de France " meetings, and those that contributed to the working groups during the OZCAR Fréjus meeting in April 2018. The authors are grateful to the  Theia technical team  ( N. Baghdadi ,  A. Sellé ,  S. Debard ) and more generally the interpole technical group led by  F. Genova , including Odatis, Form@terre, Aeris, Theia and PNDB. The persons involved in the  Trajectories  project from  Grenoble Alpes University , and in the  Zone Atelier Network  are also thanked for fruitful exchanges. The work is conducted within the  OZCAR-RI , supported by the  French Ministry of Research , French research institutions, universities and the  French Agence Nationale de la Recherche  ( ANR , project  FariTOIS   ANR-19-DATA_0003 ).