dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcomes and measures The primary outcome was the proportion of trials for which results were posted on ClinicalTrials. gov at three months. The secondary outcome was this proportion at six months. Because of the quality control process of ClinicalTrials. gov, there is a delay between the posting of results by responsible parties and the day that the results appear as posted on the website. The date and "first received date" are publicly available on the website. Consequently, we extracted these data a posteriori on 18 July 2013 for the whole cohort. This extraction was done independently in duplicate by two assessors who were blinded to the allocated intervention. All disagreements were resolved by discussion to reach consensus.
The results were presented as relative risk, risk difference, number of responsible parties needed to be contacted to obtain one additional posting (1/absolute risk reduction), and corresponding 95% confidence intervals. Results are not immediately publicly available after being sent by the responsible party to ClinicalTrials. gov (because of the quality control process). We performed a post hoc sensitivity analysis excluding wrongly included trials (that is, trials included despite their results being sent by responsible parties to ClinicalTrials. gov on the date of randomization, but were not apparent on the ClinicalTrials. gov at the time of randomization). All analyses were two sided and involved use of SAS 9. 3 (SAS Institute, Cary, NC). We considered P<0. 05 as significant.
Study population Among the 1674 studies screened at ClinicalTrials. gov, we identified 379 that had not posted results according to the FDAAA 801. These formed our cohort of trials; 271 (16%) had results posted. A random sample of 190 of these trials was allocated to the intervention group (fig  1⇓ ). Table  1⇓  shows the characteristics of trials from the whole cohort and the random sample. For 27 of the 190 trials (14%), the responsible party did not receive the allocated intervention because the email was returned with an error message.
Outcome measures At three months, 36/190 (19%) trials in the intervention group had posted results versus 24/189 (13%) in the control group (relative risk 1. 5, 95% confidence interval 0. 9 to 2. 4, risk difference 6. 2, 95% confidence interval -1. 1 to 13. 6, P=0. 096, table  2⇓ ). At six months, 46/190 (24%) trials in the intervention group had posted results versus 27/189 (14%) in the control group (1. 7, 1. 1 to 2. 6, 9. 9, 2. 1 to 17. 8, P=0. 014). The number of trials for which responsible parties needed reminding to obtain one additional posting was 10. 1 (95% confidence interval 5. 6 to 48. 7). In other words, email reminders sent to responsible parties of 10 trials led to results posted for one additional trial. A post hoc sensitivity analysis excluding 48/379 wrongly included trials (13%), with results sent to ClinicalTrials.
Discussion Sending email reminders to responsible parties of trials not complying with the requirements of the US Food and Drug Administration Amendments Act section 801 (FDAAA 801), to post basic results within one year after trial completion, did not improve significantly the posting of results on ClinicalTrials. gov at three months but did at six months. By sending email reminders to responsible parties of 10 trials, results for one additional trial would be posted at six months. A sensitivity analysis excluding non-eligible trials showed significant results at three and six months. However, the proportion of trials without posted results was high at baseline and remained high at six months. 10 13-15 18 To our knowledge, no published studies have previously evaluated any intervention to improve the posting of results on ClinicalTrials.
Implications Our trial has important implications. It aimed to be pragmatic, evaluating an intervention that we chose to be simple, easy to use, and of low cost. This intervention is based on automatically generated reminders about posting trial results, which can easily be replicated. Specific new internet tools (such as IF This Then That (IFTTT), an application allowing powerful connections to be made with this simple statement) could even be used for sending reminders. 19 To improve participation, our emails were personalized to remind recipients of the trial title, ClinicalTrials. gov NCT number, and trial completion date. However, the emails looked like a survey; the content of the email was low key, listing several possible reasons for not posting results, with penalties listed, and emails were from one of us (PR) and not from an official party such as ClinicalTrials.
gov or the FDA. These points probably decreased the efficacy of our intervention, particularly because they were addressed to already non-compliant parties. A more direct email sent by health authorities and telling recipients that they were in default and risked penalties (up to $10 000 a day) or grant funds being withheld and urging them to post their results might have more impact. Furthermore, the impact of the intervention could be underestimated, because 14% of the emails were returned with an error message. We did not find any significant difference between the intervention and control groups in the posting of results on ClinicalTrials. gov at three months; however, results from the sensitivity analysis excluding non-eligible trials were significant. This can be explained by the delay between the first submission of trial results by responsible parties and their public posting at ClinicalTrials.
gov. 7 9 This delay can vary considerably. A study of 202 posted results showed a median delay of 65 days (interquartile range 32-142) between posting and public availability of results. 20 In fact, when extracting data for the primary and secondary outcomes, we identified 48 trials (13%) with a "results first received" date that was before our date of randomization and which therefore did not meet our inclusion criteria. However, we could determine that these trials were wrongly included a posteriori, because the date when results are first received appears on the website only when ClinicalTrials. gov has publicly posted them. We could not proceed otherwise because of the validation process of ClinicalTrials. gov. The sensitivity analysis excluding these 48 trials that had been wrongly included showed a greater effect at three months (relative risk 5.
1, 95% confidence interval 1. 1 to 22. 9, risk difference 4. 9, 95% confidence interval 0. 9 to 8. 9, P=0. 02) than with the main analysis (1. 5, 0. 9 to 2•4, and 6. 2, -1. 1 to 13. 6, P=0. 096). Several reasons can explain why results were not posted. Firstly, responsible parties may not be aware of the legal requirements for posting results and the risk of penalties for non-compliance. Regarding countries under this regulation, the requirement to post results currently concerns trials of all countries, as long as one US site is involved. However, the dissemination and implementation of this law may need to be improved. These points are important to mention, when regulations concerning the publication of results and registries are being extended to the European Union. 21 In addition, navigating the ClinicalTrials.
gov register to post results can be difficult and time consuming. One study showed that approximately 38 hours was required to submit basic results on ClinicalTrials. gov, plus an additional 22 hours to collect the applicable data and information required by the register. 9 Another reason could be that some of the responsible parties are probably unaware that publishing results in a journal does not exempt them from posting results on the registry. Yet another study showed that the reporting could be more complete on ClinicalTrials. gov than in published articles, particularly for safety data. 15 Regardless, the rate of published trials among our sample was low: 8% at baseline among the 379 studies without posted results. This rate agrees with previous findings: cumulative percentages of published results were 12% at 12 months after the completion of cancer trials in the study by Nguyen and colleagues and almost half at 36 months in the same study and in the study by Ross and colleagues.
Limitations of this study Our study has several limitations. Some trials included did not meet our inclusion criteria, because the "results first received" date appears only when the results have been validated by ClinicalTrials. gov and called "posted. " To overcome this problem, we tried to access this information later (July 2013) and performed a sensitivity analysis. Secondly, because our trial aimed to be simple and pragmatic, we extracted data automatically after downloading them from the register. We might have included trials that did not fit our inclusion criteria, such as trials incorrectly registered by responsible parties as phase IV. However, this pitfall equally concerned both arms. Thirdly, we restricted our trial to studies registered as phase IV trials as to use stringent criteria, ensuring that they were under the FDAAA 801 regulation.
Fourthly, we included trials indicated as closed and might have missed trials with a "closed studies" status that had not been updated; the number of trials not complying may be larger than those we encountered. Another limitation is that for 14% of trials, the intervention was not received (returned with an error message), which highlights the need for updating email addresses of responsible parties in ClinicalTrials. gov. Also, our study focused on trials, which constituted the units of our analysis. Consequently, we did not consider that some sponsors or principal investigators could be involved in more than one trial. Finally, although we included all trials that met our criteria, the sample size was slightly lower than our sample size calculation.
Conclusions Sending emails to remind responsible parties of trials of the FDAAA 801 legal requirements to post trial results one year after trial completion is a simple and inexpensive way to improve significantly the posting of results on ClinicalTrials. gov at six months but not at three months. A direct reminder from health authorities might be more effective. Contributors: AM, IB, and PR conceived and designed the study. IB and PR supervised the study. All authors drafted the manuscript, critically revised the manuscript for important intellectual content, had full access to all of the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis. PR is the guarantor. Funding: This study received no funding. Competing interests: All authors have completed the ICMJE uniform disclosure form at www.
Activity metrics can be used to profile DEVS models before and during the simulation. It is critical to get good activity metrics of models before and during their simulation. Having a means to compute a-priori activity of components (analytic activity) may be worth when simulating a model (or parts of it) for the first time. After, during the simulation, analytic activity can be corrected using dynamic one. In this paper, we introduce McCabe cyclomatic complexity metric (MCA) to compute analytic activity. Both static and simulation activity metrics have been implemented through a plug-in of the DEVSimPy (DEVS Simulator in Python language) environment and applied to DEVS models.
However, having a means to compute a-priori activity of components (analytic activity) may be worth when simulating a model (or parts of it) for the first time. After, during the simulation, analytic activity can be corrected using dynamic one. In this paper, we introduce McCabe cyclomatic complexity metric (MCA) (already discussed in  [2] ) to compute analytic activity. Both static and simulation activity metrics have been implemented through a plug-in of the DEVSimPy (DEVS Simulator in Python language) environment and applied to DEVS models. DEVSimPy  [3]  is being developed at the SPE laboratory of the University of Corsica and is an open source project under GPL v3 license. The DEVSimPy environment is based on the PyDEVS API  [4, 5] and is aims at facilitating researches in the SPE team in order to introduce and to validate new concepts around DEVS formalism.
Activity metrics implementation DEVSimPy  [3]  is an open source project initiated by the modeling and simulation (M&S) team at SPE (Sciences Pour l'Environnement) laboratory of the University of Corsica. The aim of this software is to provide developers a collaborative M&S framework in the Python language. It uses the wxPython library which is a blending of the wxWidgets C++ class library with Python. The DEVSimPy M&S kernel is based on the PythonDEVS  [4]  API which offers a consistent and coherent set of classes in order to construct a modular system and to achieve its hierarchical simulation. A plug-in manager is proposed in order to expand the functionality of DEVSimPy allowing their enabling/disabling through a dialog window. Built upon definitions of activity metrics given in the previous section, DEVSimPy implements a new plug-in called Activity Tracking (AT).
Conclusions In this paper, analytic and simulation activity metrics have been presented with a special attention about their relationships. Concerning the analytic activity metric, the new McCabe cyclomatic complexity activity metric (MCA) of a DEVS model are presented. For simulation activity metrics, the well known quantitative-activity metric (QA) has been used in the context of profiling the activity distribution over components. Both MCA and QA metrics have been compared with the user CPU time of the DEVS transition functions are introduced. The MCA, QA and CPU metrics have been implemented through a plug-in of the DEVSimPy (DEVS Simulator in Python language) environment and applied to a case study. It is clear that MCA constitutes a good approximation allowing a-priori estimation of component simulation overheads.
Main perspective concerns the improvement of the simulation algorithm using coupling metrics and associating it with activity. Structure modification of abstract simulators will be guided by a combination of both activity and the coupling metric associated with a parallel and distributed simulation algorithm. DEVSimPy framework already integrates PyPDEVS API  [5]  thus exploiting parallel and distributed features. We plan also to use an activity predition model  [14]  which requires that the user provides (domain-specific) knowledge about how a specific model will use computational resources when simulated with a specific simulator. Futhermore, in order to avoid the break of the model-simulator abstraction (due the fact that the modeller needs some knowledge about the simulator's operation), we plan to explore the use of Domain-Specific Language (DSL) to automated the construction of the activity prediction model.
As stated in the "Instructions to Authors" of almost all scientific journals, material, data and protocols should be made available in order to allow others to "replicate and build upon the authors' published claims" (Anonymous 2014). While deposition of genomic sequences in public databases and of type strains of validly named prokaryotic species in public resource centers are two examples for a functioning implementation, deposition and release of material and data are, in practise, frequently left to the authors' discretion. Some journals may have a stricter implementation policy than others but generally enforcement mechanisms do not exist in those frequent cases where authors deny release of requested material by either not responding or by stating, among other arguments, intellectual property priorities, loss of material, biosecurity issues, export and import regulations, or patent issues.
Of 20,200 prokaryotic strains reported in 835 articles in eight European journals in 2008, only 190 strains (0. 94%) were deposited in public collections. What are the reasons behind this and why are the huge investments in publicly funded research not protected for confirmation of results and future use? It is evident that the biological materials on which published data has been generated must be available to check when aberrant or erroneous results are discovered or when new technologies are available for further study and characterization. The low level of deposition of prokaryotic strains into today's public service collections (the microbial domain Biological Resource Centre -mBRC), and bad experiences of participants when attempting to access biological resources post publication, triggered discussion on a strategy to enhance and facilitate access to microbial resources.
In addition to the safeguarding of this biological material, access to these resources will be facilitated; all recognized mBRCs maintain easily accessible databases, in which strains and associated data including origin of the strains as well as depositor and cognate publications are searchable. Initiatives that allow searches among the resources and data of several mBRCs simultaneously to make them more consultable (CABRI, WDCM and in the future MIRRI-Information System) have been undertaken to widen the access to these resources making them more likely to be utilized in research and thus increasing the chances to develop scientific collaborations and industrial exploitation of the resources. The workshop participants decided against a mandatory post-publication deposition of microbial strains but agreed on a set of criteria based on the phylogenetic, metabolic and genomic uniqueness of "key" strains worthy of deposit.
Acknowledgements The authors thank additional members of the 2011 Braunschweig workshop:  Jörg Overmann ,  Esperanza Garay-Aubán ,  Peter Kämpfer ,  Yohan Lecuona ,  James I Prosser ,  Ramon Rosello-Mora ,  Karl-Heinz Schleifer , and  Kornelia Smalla. The workshop was an initiative of the  European Consortium of Microbial Resource Centres (EMbaRC) , supported by the  European Commission 's  Seventh Framework Programme  ( FP7 ,  2007 ( FP7 , -2013)),  Research Infrastructures action , under the grant agreement No. FP7-228310. This communication received funding from the  European Union's Seventh Framework Programme for Research, Technological Development and Demonstration  under grant agreement no  312251. Additionally, thanks go to all those attending the February 2014 MIRRI Heads of Collections meeting who participated in the discussions, including some of those above plus  Pedro Crous ,  Edward Moore ,  Oleg Stupar ,  Chantal Bizet ,  Dominique Clermont ,  Rosa Aznar ,  Paul Devos , and  Anna Misiewicz.
Scientists are confronted with significant data management problems due to the huge volume and high complexity of environmental data. An important aspect of environmental data management is that data, needed for a process, are not always in the adequate format. In this contribution, we analyze environmental data structure, and model this data using a semantic-based method. Using this model, we design and implement a framework based on Web services for transformation between massive environmental textbased data and relational databases. We present a mapping model for environmental data transformation to be used in the scenario devoted to the methodology for development of stochastic models for prediction of environmental parameters by application of Gaussian processes.
However, this form presents some drawbacks:  Low readability: data can be presented in different units without any context-based explanation and they become somewhere ambiguous.  Hard to integrate: scientific data are natively heterogeneous, unstructured and they are usually stored in different files and/or in different locations. This makes it difficult to integrate all the data into one place without a common sematic schema.  Data searching: content discovery is a difficult task in a large datasets or in thousands of distributed files. An important aspect of environmental data management is that data, needed for a process, are not always in the adequate format. Scientists use different tools in different stages of their research; they develop some tools for their work by themselves and spend time to retrofitting data into acceptable formats for these tools  [4].
www. ijacsa. thesai. org So, the main problem to address here is how to provide an efficient way to implement massive data transformation between texts and databases. This is a common problem for both computer science researchers and environmental science researchers, as we consider environmental data as a subset of scientific data. In semi-structured data, the information that is normally associated with a schema is contained within the data  [3]. The meaning and logic structure of semi-structured data can be expressed and identified by semantic tags. For instance, XML is a standardized extended markup semi-structured data. In this paper, we present our work in progress. We analyze environmental data structure, and model this data using a semantic-based method. Using this method, we design and implement a Web service-based framework for transformation between massive environmental text-based data and relational databases.
B. Scientific Languages The Apache Hadoop  [9]  is an open-source software library for storage and large-scale processing of data-sets on clusters. It is a framework that allows distributed processing of large data sets across single servers or thousands of machines by using simple programming models. As presented in  [8] , the Java open source library is designed to detect and handle failures at the application layer, so delivering a highlyavailable service on top of a cluster. Google Open Refine (GOR)  [6]  is a standalone open source desktop application for data cleanup and transformation to other formats. It is similar to spreadsheet applications; however, it behaves more like a database. GOR opens a Web interface powered by a Web server. It operates on rows of data which have cells under columns, which is similar to relational database tables.
Transformation expressions are written in the GOR Expression Language. It is able to work with CSV, TSV, XML, JSON, Excel and RDF formats. Matlab  [7]  is a numerical computing environment and allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces. It imports data from CSV files, excel spreadsheets and databases. Import functions read the data in memory and reorganize them in vectors or matrices, then all functions work on these data structures and possible interfacing with programs written in other languages, including C, C++, Java, and FORTRAN.
C. Scientific Workflows Workflow composition represents the conceptual model of a scientific analysis which implies the flow of data within a system. Every step of workflows acts on the data. The required data are obtained from previous steps, from local files, from relational databases, from remote services or another source. Kepler  [10]  is a free scientific workflow management system. It is able to acquire data from different sources, process them by prepared or user defined components. Optionally, an external data processing facility can be applied. This software provides process and data monitoring, provenance information, and data movement solutions. Its architecture is directed graphs where the nodes represent discrete computational components and the edges represent paths along which data and results can flow between components.