dataesr/scientific-paragraphs-categorization huggingface dataset:
If we formulate such a query in HIFUN, it will be translated to a SPARQL query that will be evaluated successfully; however the results will not be complete, i. e. , only the invoices for which the path delivers. brand. f ounder. birthYear exists will be considered. Moreover, we may encounter the problem of multiple values, i. e. , when a brand was founded by more than one person. In that case, even if our dataset contained complete information, the path delivers. brand. f ounder. birthYear would not be functional. If we formulate such a query in HIFUN, it will be translated to a SPARQL query that will be evaluated successfully; however the results will not be accurate, i. e. , all paths will be taken into account; valuating more than one birth year per product, not only one birth year per product.
brand. f ounder. birthYear. avg); note that denotes the empty grouping function, since in our example we do not want to group the results, just to apply AVG to the entire set. To compute this feature we can use a subquery that returns two variables, one for the objects and one for the corresponding feature value, say v f 1 and v f 2 respectively, while ensuring that v f 1 should the same with the variable used in the outer query for these objects. In our example, the corresponding SPARQL query that computes the sought answer (where the subquery provides two variables), is the following: The key point is that the location of the director will be considered, since it is inferred that (p1,worksAt,brand1). Such inference would not be possible if we translated the data to the relational model. The ability to leverage the inference rules of RDF Schema in the context of analytic queries is especially important for datasets which are described with ontologies that contain high number of subClassOf and subPropertyOf relationships for achieving semantic interoperability across various datasets, like those in the cultural and historic domain  [7, 42].
3), or after a transformation (Section 5. 4). In the former case, the user does not have to do anything: all resources of the RDF dataset and all properties can be exploited as an analysis context. In the latter case the intended transformation can be made by tools like LODSyndesisML  [40]  that allows the user to interactively select the desired features. The output of this step can be either (a) a csv file or an RDF file in RDF Data Cube format that contains the materialization of the defined features, or (b) a set of feature specifications each being a pair (feature name, SPARQL query) which will be exploited in the translation process as discussed in the last part of Section 5. 4 and in Section 6. 6. As regards S 2 q , and assuming that an analysis context has been specified, we developed a tool, called HIFUN RDF , where the user is asked to select the functions of a HIFUN query i.
The translated to SPARQL query is then executed on the triple store OpenLink Virtuoso (https://virtuoso. openlinksw. com/) (where the input data has been uploaded) and the returned results are displayed and saved in a ". csv" file in the form of -var 1 , var 2 ,. , var i , TOTALS. As regards S 4 vis , we embedded in HIFUN RDF the jfreechart library (https://www. jfree. org/jfreechart) for reading the results of the SPARQL query and preparing various charts including line, bar, pie and 3D pie charts. Three screenshots are shown in Figure  4  that visualize the results of the query "total quantities by branch" over a synthetic RDF dataset that uses the same schema with our running example. The indicative flows that are possible with this implementation, are illustrated in Figure  5. The figure includes a workflow where the process starts with selecting the data set the user wants to analyze using tools like Facetize  [43]  and LODSyndesisML  [40] , suitable for discovering, cleaning, organizing data in hierarchies.
For steps (ii) and (iii) we plan to investigate extending the core model for exploratory search over RDF that is described in  [12]  (for exploiting its capabilities of forming conditions that involve paths and complex expressions), with actions for supporting S 1 ctx and S 2 q (that are not supported by that model). That will enable the user to specify interactively and with simple clicks complex restrictions that may concern the set of resources of the analysis context, and/or the various restrictions of the analytic query (of grouping or measuring functions, or of the final results). Finally, we should mention that an interactive system for analytics that uses HIFUN as the intermediate layer, enables the formulation of analytic queries over data sources with different data models and query languages, since there are already mappings of HIFUN to SQL (for relational sources), and to MapReduce using SPARK  [46] , thus our work enables applying that model over RDF data sources too.
Concluding Remarks In this paper, we elaborated on the general problem of providing interactive analytics over RDF data. We showed the motivation for this direction, we described the main requirements and challenges, and we discussed the work that has been done in this area. Subsequently we investigated whether HIFUN, a functional query language for analytics, can be used as a means for formulating analytic queries over RDF data in a flexible manner. To this end, we analyzed the applicability of HIFUN over RDF, we described various methods that can be used to apply HIFUN over such data, and then we focused on the problem of translating HIFUN queries to SPARQL queries, starting from simple queries and ending up to complex queries. We discussed what happens when HIFUN cannot be applied, and how features can be employed to tackle properties and paths that are not functional.
Acknowledgements  J. -J. Sinou  acknowledges the support of the  Institut Universitaire de France. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party.
Materials and Methods Participants. Seventy-four right-handed young healthy adults (34 males; mean 6 SD age, 23. 4 6 5. 2 years) participated in the experiment for monetary compensation. The sample size was calculated based on power calculations for a companion fMRI study  (Yau et al. , 2020)  that recruited from the same pool of subjects. No separate power calculations were performed for this EEG study, but we achieved a sample size three to four times that of other studies with EEG and evidence accumulation  (Cavanagh et al. , 2011a; O'Connell et al. , 2012; Kelly and O'Connell, 2013; Pisauro et al. , 2017; Steinemann et al. , 2018). All subjects gave informed consent before data acquisition and were screened for current or past diagnosis of a psychiatric disorder, neurologic disorder, or concussion, and moderate to severe depression  (Beck Depression Inventory score,.
In easy trials, facial expressions gradually morphed toward one of the two emotions. In ambiguous trials, facial expressions remain relatively neural until two-thirds of the trial has elapsed, after which point emotion rapidly ramped up toward happy or sad. time-jittered fixation cross. Trials always began with a neutral expression and gradually transitioned into either a happy or sad emotion. Participants were instructed to predict whether the facial expression would be happy or sad by the end of the trial using their index and middle finger, respectively, via a button box in their right hand, and to respond whenever they felt confident enough to do so. Subjects were asked to respond both as quickly and as accurately as possible. Face stimuli were derived from the NimStim database  (Tottenham et al.
, 2020) , we observed two distinct groups of individuals based on performance under the ambiguous condition (Fig. 2 ), as follows: (1) early responders (ERs; n = 32), who on !80% of ambiguous trials responded during the first two-thirds of the trial before information ramped toward one direction and (2) the rest, who were categorized as late responders (LRs; n = 25). We hypothesized that one factor that may drive this group difference lies in differences in an endogenous urgency signal. EEG acquisition and preprocessing. EEG was recorded continuously using a 256-channel high-impedance HydroCel Geodesic Sensory Net and the NetStation 5 acquisition software (Electrical Geodesic). As per manufacturer standard recommendations, electrode impedance levels were kept to ,50 X during acquisition. Data were collected with a sampling rate of 1000 Hz using the electrode Cz as a reference with online visualization filters of 60 Hz for notch, 5 Hz for high pass, and 120 Hz for low pass.
e. , theta and alpha, detailed below in the subsection Time-frequency analysis). Third, artifacts related to eye blinks and muscle movement were corrected for using the Multiple Artifact Rejection Algorithm (MARA) -a supervised learning algorithm that uses independent component analysis to detect and eliminate artifacts based on established expert ratings  (Winkler et al. , 2011). Finally, the previously excluded bad channels were interpolated, and the data were downsampled to 250 Hz for computational efficiency. Data quality after preprocessing was assessed automatically by Automagic and confirmed by subsequent manual inspection. Further details regarding the Automagic pipeline can be found online (https://github. com/methlabUZH/automagic). After preprocessing and quality control, data from 57 participants were used for further analysis.
, 2019)  as well a motor beta signal in the hemisphere contralateral to the hand for response execution  (O'Connell et al. , 2012). While midgamma-band activity (48-66 Hz) has also been implicated in containing evidence accumulation signals  (Polanía et al. , 2014) , we were unable to test this because for technical reasons our data had to be low-pass filtered at 50 Hz. We were interested in testing whether our straightforward, minimalistic processing analysis of ERPs was linked to these ongoing oscillatory fluctuations. To this end, we used the Morlet wavelet methods implemented via mne. time_frequency. tfr_morlet to assess spectral power across the trial period on our preprocessed epoch data. The estimated power of the trial was then baseline corrected for the 500 ms preceding stimulus onset.
For visualization purposes, the data were resampled using scipy. signal. resample to match onset and response across all trials (i. e. , all trials began at time point 0 and ended at time point 1, which stood for the maximum reaction time (RT) for the subject). Power was averaged across the frequency range for each band (delta band, 0-4 Hz; theta band, 4-8 Hz; alpha band, 8-12 Hz; beta band, 12-30 Hz; gamma band, 30-45 Hz). Each sample of EEG time course was z scored, and outliers (z. 4. 5) were replaced with the average EEG power  (Frank et al. , 2015). We then extracted the early (first 10% of trial time after onset) and late (last 10% of trial time before response) power for each frequency band to compare against ERPs of interest. Based on the cited literature, we focused on theta power from the FCz, alpha power from the Pz, and beta power from electrodes of the left hemisphere.
4a ). The shape of the RT distribution determines the decision parameters  (Ratcliff et al. , 2016). Here, we applied a hierarchical estimation of the DDM (HDDM;  Wiecki et al. , 2013) , implemented in Python 2. 7 (http://www. python. org), to calculate the decision parameters (Fig. 4b ). The hierarchical design assumes that model parameters from individual participants, while varying, are not completely independent. Rather, parameters of individuals are drawn and constrained by priors based on the group distributions  (Gelman et al. , 2013). This Bayesian estimation is thought to be more robust in recovering model parameters, particularly when the number of trials is relatively small  (Matzke and Wagenmakers, 2009; Wiecki et al. , 2013). Trials that fell within 5% of each tail of the RT distribution were considered outliers that cannot be captured by HDDM (e.
Blue nodes represent observed data, including trial-wise behavioral data (accuracy, RT) and neural measures (P300 and CPP). Trial-by-trial variations of v and a were modulated by P300 and CPP, respectively, as well as by trial type (i. e. , easy or ambiguous trials). independent urgency signal that grows in time  (Cisek et al. , 2009; Drugowitsch et al. , 2012). We constructed a minimalistic UGM and an urgency-free, nonhierarchical DDM to compare against and to test whether accounting for an urgency signal may better fit our observed data. In both models, a filtered evidence variable, x, was derived by the following differential equation: t dxðtÞ dt ¼ Àx t ð Þ 1 gE t ð Þ 1 G 0; N ð Þ: (1) At any given time t, the evidence E (i. e. , level of information/facial emotion level) is multiplied by an attentional fixed gain term g.
Linear mixed-effect models implemented via the Python function statsmodels. mixedlm were used to examine the relationship between the observed CPP signal compared with a neural code predicted by either the UGM or the DDM at the trial level. Subjects were included as a random variable with differing intercepts. For both the predicted and observed neural signals, buildup was determined as the cumulative sum of activity from 500 ms post-stimulus onset to the time of response. This 500 ms delay was to ensure that we did not confound CPP activity with the P300 signal, as they overlap spatially. The log-transformed absolute value of the predicted neural code was used since CPP is assumed to be a general evidence accumulation signal that positively ramps up regardless of the stimulus presented. As with the HDDM, trials in which participants registered no response or with RTs that fell within 5% of each tail of the RT distribution were considered outliers and were discarded.
Statistical analysis of behavioral data. Statistical analysis was conducted with packages pingouin  (Vallat, 2018) , statsmodels  (Seabold and Perktold, 2010) , and mlxtend  (Raschka, 2018)  in Python 3. 7 (http:// www. python. org). To account for potential spurious outliers, nonparametric tests were used to assess subject-level data. Mann-Whitney U tests and Wilcoxon signed-rank tests were conducted to compare differences in behavioral performance (i. e. , mean accuracy and RT) among emotion, groups, as well as neural signals between correct and incorrect trials. Spearman correlations were used to examine relationships between different neural signals (e. g. , P300 and CPP) and other metrics of interest (e. g. , HDDM decision threshold, DDM decision threshold, and UGM urgency signal). An a of 0.
05 was used as the threshold for statistical significance, and results were corrected for multiple comparisons using the false discovery rate Benjamini-Hochberg correction, as implemented by function statsmodels. stats. multitest. multipletests. Software used. The following software was used: Python Programming Language (RRID:SCR_008394); Pingouin (https://github. com/raphael vallat/pingouin); Statsmodels (RRID:SCR_016074; https://www. statsmodels. org/dev/index. html); Mlxtend (https://github. com/ rasbt/mlxtend); MNE-python (https://mne. tools/stable/index. html); Automagic (https://github. com/methlabUZH/automagic/wiki); and HDDM (https://github. com/hddm-devs/hddm).
A 2019 survey of RCTs registered on ClinicalTrials. gov by funders with a data sharing policy found that data sharing plans were present for 77% (95% CI: 67% to 84%) and 81% (95% CI: 72% to 88%) of RCTs funded by noncommercial and commercial funders, respectively  [9]. An expressed intention to share data was found in 12% (95% CI: 7% to 20%) and 59% (95% CI: 49% to 69%) of RCTs funded by noncommercial and commercial funders, respectively.
g. , data sharing)  [14]. Furthermore, authors are often difficult to contact and may lack the time, knowledge, technical infrastructure, or financial resources to prepare and share the data sets  [3]. In addition, authors may not be aware of the resources available to facilitate data sharing and may face legal difficulties in sharing their data, as the definition of "anonymization" is not universal (e. g. , in the European context), can be ambiguous, and carries a risk of loss of information  [15]. It is also possible that data are not requested; indeed, usage metrics from repositories such as YODA and CSDR suggest that a large majority of data is not requested or that reuses may remain unpublished. Practical issues could contribute to this problem, as the process of data sharing on these platforms is still cumbersome, and data shared without codes or proper dictionaries may be impossible to reanalyze and reuse.
A centralized approach toward monitoring of data sharing is needed, where data sharing dashboards would collect these data sharing metrics (e. g. , intention to share, data sharing, and data reuse) from journals, funders, and repositories and then present it to the research community. Currently, this information is siloed (e. g. , each data sharing platform presents this information separately) and is not presented broken down by journal and by specific journal articles. Recent efforts have been made to monitor data sharing, and other open science practices, in journal articles  [19] , and these tools (https://github. com/quest-bih/oddpub) are already used at the QUEST center for transforming biomedical research in Berlin, Germany. These initiatives need to be generalized and be included in ICMJE policy.
The challenges identified, together with suggested changes to ICMJE policy and details of the necessary evaluations, are listed in Table  1. We argue that the current practice of journals listed as ICMJE affiliated without committing to its policies undermines the value of the affiliation. A basic certification reflecting policy implementation could increase commitment. For example, the "TOP factor," an indicator dedicated to assessing transparency, openness, and reproducibility (https://www. topfactor. org/), could provide a certification of this nature. Specifically, a TOP factor of 3 indicates that the policy includes both a requirement and a verification process for the correspondence of data with the findings reported in the paper. For this purpose, each journal could have a Reproducible Research Editor, like the Biometrical Journal, for instance, with specific infrastructures to submit data and analyze manuscripts provided on the journal's web-based submission platform.
Suboptimal intention to share data by RCTs in clinical trial registration on databases such as ClinicalTrials. gov for funders with a data sharing policy for RCTs. Policies should require the use of registries making intention to share data mandatory. Monitoring improve research transparency. Together, such initiatives can be seen as primary steps in incentivizing data sharing behaviors. Currently, the ICMJE policy only encourages data sharing but does not guarantee it. A more binding policy to favor data deposition whenever ethically possible is needed. We believe that the policy should include adequate incentives for data sharing, as part of hiring, promotion, and tenure of researchers, together with reinforcement measures that journals can adopt in case of noncompliance with data sharing requirements.
FN received funding from the  French National Research Agency for the ReiTheR (Reproducibility in Therapeutic Research)  project, that this article was part of. Project Number:  ANR-17-CE36-0010. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. this manuscript have the following competing interests: the authors have declared that no competing interests exist. CA received postdoctoral grants from  Uppsala University , the  Blanceflor Foundation , and the  Swedish Society of Medicine. In the past 36 months, JDW received research support through the  Collaboration for Research Integrity and Transparency  from the  Laura and John Arnold Foundation  and through the  Center for Excellence.
, 2015; Sen et al. , 2014). Building on these methods, in this paper, we 1) elucidate the development of a measurable targeting metric (i. e. , group cohesiveness) that combines measures of pairwise tie-strength and methods of group detection, based on longitudinal collective physical movement dataset (indoor location traces of individuals connected to the Wi-Fi in a large Asian university); and then 2) assess the effectiveness of a targeted mobile promotion offer based on this new metric. Specifically, to align with the current practice where retailers choose the timing of coupon redemption (e. g. , now vs. later), we deployed this measurement in a field experiment where a consumer received push promotions of varying redemption lengths via a mobile app. The results of the experiment indicate that consumers who are in a highly cohesive group (e.
Group Detection using Mobile and Wireless Sensing Group context detection and monitoring based on passive sensing have garnered significant interest in the ubiquitous computing literature in recent years. Eagle & Pentland (2006)  infer relationships amongst workplace colleagues through location and proximity data sensed through smartphones. Brown et al. (2014)  use sociometric badges to detect social encounters to study the impact of building design on such encounters. Sen et al. (2014)  captured instantaneous groups through longitudinal indoor movement behavior of its occupants, derived from passively observed Wi-Fi data logs in two large shopping malls and a large metropolitan campus. In the current work, we use indoor location/movement traces, derived from Wi-Fi networkbased logs passively collected by a commercially deployed Wi-Fi infrastructure, to infer two different facets of group context: (a) Over a longitudinal observation period (e.
We find similarities between our definitions of tie-strength based on co-locations and other definitions in prior literature dealing with different datasets. The Bow-Tie framework proposed by  Mattie et al. (2018)  defines tie-strength (or the weight of an edge between two persons in an undirected network) as the duration of calls between two persons, computed from their call data record (CDR) dataset. In our framework, the temporal precision is defined in a similar fashion -to measure the total duration of physical "co-locations" or "meetings" (as opposed to calls). Our definitions of temporal precision and spatial precision are comparable to two measures proposed by  Granovetter (1983) : time spent interacting with someone and the level of intimacy (i. e. , we weigh a co-location event involving fewer people higher (or more intimate) as opposed to colocation events where a larger number of individuals are present), respectively.
Analyses and Results We ran a moderated regression model using the PROCESS model  (Hayes, 2013)  in SPSS (Model 1) with the likelihood of redemption as the dependent variable and promotion lead-time and group cohesiveness as the independent variables (F(3,106) = 2. 10, p = 0. 11). The results show that neither group cohesiveness nor the promotion lead-time had any main effect on the participant's likelihood of redeeming the offer (p > 0. 11 in both cases). However, as hypothesized, there was a significant interaction between the two variables (b = 0. 00, S. E. = 0. 00, t = 2. 50, p = 0. 01). To understand the nature of the interaction, we looked at the effects of group cohesiveness on the likelihood of redemption, separately for longer versus shorter promotion lead-time conditions. We find that, for a promotion message with a longer lead-time, group cohesiveness has a marginally negative effect on the participant's likelihood of redeeming the promotion offer (b = 0.