dataesr/scientific-paragraphs-categorization huggingface dataset:
If insufficient information is provided for a patient, the system can create a new "non-matchable" entry. The data are de-identified for the current patient, but no future patient data will be linked to the entry. This process limits the risk of erroneous match. Identities of the patients, their unique CARPEM identifier, as well as identifiers across the participating institutions, are recorded in a relational database. However, the interaction between the platform and the patient database is performed through a web service, limiting the risk of mistakes during the data processing (only a strictly limited number of functionalities are allowed through the web service).
De-identification The French law does not provide a list of identifiable variables that should be removed from patient data to ensure anonymization. We used the HIPAA Safe Harbor [33] recommendation as a guideline. For each patient, we remove all directly identifying data and we choose a random number between -365 and 365 to shift all dates. The date-shifting value is stored in the patient database. Identifiable data and clinical data are kept in separate databases installed on separate servers.
Data storage and management We leverage the i2b2 platform  [7]  to store our data. The i2b2 data model is composed of 6 main tables organized in a star schema. For each new record, we collect and store information on the provider (who generated the data) and the provenance (the exact context of the data generation), as well as detailed information about the measured variables. Data that cannot be efficiently stored in the i2b2 format (e. g. images or gene expression data) are stored as references to files on our local file system or on the hospital information system. Not all data are accessible to all users. The platform manages transversal projects as well as project managed by a small group of researchers. In the CARPEM translational research platform, data and data-access are handled by "project".
Data access layer For the final users of the platform, researchers and clinicians, the data-integration layer is a blackbox. The only visible part is the data access layer. We provide tools to the users for the exploration and data-driven hypothesis generation. We leverage tranSMART to allow clinicians and researchers to explore their data, without complex interaction with bioinformaticians and biostatisticians. TranSMART allows data exploration through a web application as well as data interrogation through an API. It can be used by users with programming skills as well as without any expertise in computer science. Additional software and tools will be deployed in the future to respond to the users need.
Ethics, legal and data-property issues Ethics Ethics is a core concern of the construction of the platform. The CARPEM program adopted a dual solution. Data collected during the care process are integrated under an opt-out consent policy. Patients who wish not to be included in research projects can express their request at any time during or after their visit at one of the hospitals. For research data (retrospective, prospective, 'omics' and so forth), an opt-in solution has been adopted. Users of the platform (i. e. clinicians and researchers) have to accept a user-agreement, in which they are reminded of basic ethical and common-sense principles: respect of private information, use the data in a secured environment, interdiction of patient re-identification, interdiction to share their personal identifiers.
Data property Datasets are classified in two categories: public and private datasets. Public datasets are shared without restriction. Private datasets are accessible only to the project's PI by default. The PI or a mandated member of the data-provider's team may give access to all or part of a datasets, to one CAR-PEM researcher or a group of CARPEM researchers. To be eligible for data-sharing, researchers need to have complied with official obligation, and to have accepted CARPEM data user agreement. For retrospective datasets, the identification of data ownership is clear. For clinical data from hospitals, ownership is discussed within the clinical working groups, prior to the collection. By default, the clinical teams implicated in the collection of the data are considered owners.
Adoption The CARPEM translational platform is young. Out the eight working groups by cancer localization, only four have started the identification of shared minimum datasets, and the integration of data in the platform. Moreover, the platform has been mostly dedicated to the integration of retrospective datasets during its first year. Five clinical and research groups have contributed 13 datasets. Six datasets have been loaded into tranSMART, three in i2b2, representing a total of 5,700+ patients, and a little over 300,000 database records (including 24,500 diagnosis, 19,900 procedures and 57,300 biobank items). The REDCap eCRF software currently handles 10 ongoing studies. The integration of data streamed from hospital IT is at an early stage of development and includes billing codes (ICD10 data and normalized procedures).
Expected benefits The objective of data sharing has already led to harmonization efforts. Clinicians and researchers who plan to pool their data and increase the statistical power of their studies, thanks to the CAR-PEM platform, have defined common data elements that will be collected in prospective data sets. Data harmonization efforts concern all items, including clinical phenotype descriptions, surgical reports, imaging reports, treatment and follow-up, genetic data, and adverse events. The meetings dedicated to data sharing tools will facilitate and support the continuous process of harmonizing the data collection among the participating hospitals within the CARPEM program. By providing efficient data management and user-friendly exploration tools we hope to generate even more research hypotheses.
Federated query The architecture presented in this article focuses on the integration of data in one single data warehouse. In specific cases the transfer of data might not be possible; e. g. because of regulations, or data ownership dispute. Federated queries might be helpful for such cases. For example, SHRINE  [26]  -a federated querying system for i2b2 -could be used for cohort selection, and could enable result sharing without having to share actual datasets.
Future work The translational research platform is at an early stage of its deployment in the CARPEM program. Although we are confident that its architecture and functionalities will help the researchers in the data exploration tasks while ensuring an efficient data management and quality control, the use of the platform for large scale cohorts remains to be assessed. Moreover, with high-throughput technologies being more and more involved in the everyday care of patients, we expect a dramatically increasing amount of data being available per patient, with an associated responsibility for the researcher to make use of such data. Big-data technical solutions (such as MapReduce, or NoSQL solution including the ADAM genomics analysis platform leveraging the Apache Spark technology) might become a mandatory step in the construction of translational research platforms in a near future.
Conclusion We designed and built a translational research platform for the CARPEM cancer research program. We evaluated the needs and requirements of translational research and outlined scientific and technical principles that guided us in the construction of the platform. Technically, the organization of the platform can be summarized as follows: we standardize inputs into a single exchange format, then de-identify the data and integrate them into an i2b2 data warehouse. We then push datasets to exploration tools such as tranSMART.
Clinical trials and basic-science studies without statistically significant results are less likely to be published than studies with statistically significant results. Systematic reviews and meta-analyses that omit unpublished data are at high risk of distorted conclusions. Here, we describe methods to search beyond bibliographical databases to reduce evidence selection bias in systematic reviews. By searching conference proceedings, unpublished studies may be identified. Moreover, clinical trial registries -databases of planned and ongoing trials-, and regulatory agency websites -such as the European Medicine Agency (EMA) and the United States Food and Drug Administration (FDA) -may provide summaries of efficacy and safety data. Primary and secondary outcomes are pre-specified in trial registries, thus allowing the assessment of outcome reporting bias by comparison with the trial report.
Introduction As highlighted in related Research Techniques Made Simple articles, reporting bias remains one of the greatest threats to the validity of systematic reviews (2016;  Abuabara et al. , 2012). To obtain a fair assessment of the effects of an intervention, systematic reviews of interventions for skin diseases should use stringent efforts to include all relevant evidence. An exhaustive search of trials is the most important step in systematic review methodology to reduce evidence selection bias. Yet many published articles labeled as "systematic reviews" search only a fraction of the evidence by limiting the search to one or two convenient databases. In this article, we describe a workflow for searching sources beyond bibliographical databases  (Figure). These techniques will be useful for systematic reviewers for planning an optimal search strategy and for readers of systematic reviews to judge whether suboptimal methods to identify trials may have introduced bias.
First, find the published trials For a systematic review of dermatological interventions, the least one can do is to make every effort to identify all published randomized trials. Searching the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and EMBASE will likely allow the researcher to find the majority of published trials. CENTRAL is particularly important to search because it offers a concentrated source of reports of randomized trials. Other specialized bibliographical databases may be relevant to specific topics (Online Appendix 1). Searching bibliographical databases should follow the methodological principles for information retrieval  (Lefebvre et al. , 2011). In particular, search equations should seek increased sensitivity and use ad hoc filters to identify randomized trials (such as the Cochrane Highly Sensitive Search Strategies or filters listed at https://sites.
Next, find the unpublished trials About 50% of clinical trial results that are presented at meetings and congresses remain unpublished  (Scherer et al. , 2007). As a consequence, conference abstracts should be searched in order to identify trials with unpublished results. Data reported in conference abstracts may be not be reliable as full publication, as abstracts may contain preliminary results and may not contain sufficient information to assess methodological quality. However, abstracts allow documenting the existence of unpublished trials (more specifically, their number and sample size), and unpublished outcomes. It allows statistical analysis to gauge the sensitivity of the systematic review conclusions to the non-dissemination of these trials. Some databases index conference proceedings.
Systematic review authors can search the World Health Organization International Clinical Trials Registry Platform Search Portal, which gathers records of trials registered on 16 data providers including clinicaltrials. gov and the European Union (EU) Clinical Trials Register. Besides institutional registries, pharmaceutical companies have also developed clinical trial registries. When a relevant completed trial is identified but no published article can be matched, the systematic review authors can contact the trialists or sponsors to inquire about the trial status and ask for results. Some authors have even suggested that only prospectively registered trials should be included in meta-analyses because the risk of bias with any other form of trials is too great  (Roberts et al. , 2015). Trial registries also allow identifying unreported outcomes since the primary and secondary outcomes are documented in each trial record.
Clinical trial registries may also contain summary trial data. In clinicaltrials. gov, the results of applicable clinical trials as defined by Section 801 of the Food and Drug Administration Amendments Act are required to be posted and the results of many other trials are also posted voluntarily. For systematic reviewers, it is therefore crucial to use clinicaltrials. gov to find trial results, in particular safety information. The European Medicines Agency (EMA) has also enacted a proactive publication of summary results through the EU Clinical Trials Register. Some pharmaceutical companies have also developed their own clinical trial result databases.
Moreover, the goal to obtain reporting transparency will be reached as prominent journals continue to establish clear requirements for making trial data available  (Taichman et al. , 2016). The clinicalstudydatarequest. com and the Yale University Open Data Access websites allow researchers to request access to individual patient data and supporting documents from industry-sponsored clinical trials. Moreover, the European Medicines Agency policy has released guidance on the publication of clinical data for medicinal products. This policy has entered into force in 2015 for the publication of clinical reports but, in a later stage, it will also concern the publication of individual patient data. Systematic reviews must adapt to these improvements and cover the multiple new information sources in their search strategies.
Summary points -Trials without statistically significant results are less likely to be published than trials that show apparent differences (publication bias). Moreover, trial outcomes that do not support the use of the new treatment are less likely to be published than those that do support its use (outcome reporting bias). -Systematic reviews and meta-analyses that omit unpublished data are at high risk of biased conclusions. To increase their validity, systematic reviews should rely on a thorough search for published and unpublished trials. -The Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE should be searched for published trials. -Sources for finding unpublished trials have expanded recently. Conference proceedings, clinical trial registries, regulatory agency reviews, and health technology assessment reports should be searched for unpublished trials.
We searched a range of bibliographical databases, including the Cochrane Central Register of Controlled Trials (CENTRAL); the specialized registers of the Cochrane Infectious Diseases Group, the Cochrane Skin Group, and the Cochrane Sexually Transmitted Infections Group; MEDLINE, EMBASE, and LILACS, with no restriction on language or date. […]Wescreened the reference lists of all selected trials. We contacted the main authors in the field to identify any additional published or unpublished data. We searched the proceedings of the following conferences: European Congress International Union Against Sexually Transmitted Infections (IUSTI),[…]. We contacted the pharmaceutical companies […] and searched the clinical trial results database of each company to […] identify ongoing and unpublished trials.
We also searched the search portal of the World Health Organization International Clinical Trials Registry Platform. " Example from Cochrane review "Workflow CD009036 on genital herpes A. First, find the published trials Cochrane Database Syst Rev. 2014;8:CD009036. doi: 1) Bibliographical databases (CENTRAL, MEDLINE, EMBASE) 2) Reference lists of all selected trials 10. 1002/14651858. CD009036. pub2). Details can be found in Online Appendix 1 and a 3) Systematic review databases (Cochrane Database of Systematic Reviews, tutorial to search FDA drug approval packages and EMA public assessment reports can be Database of Abstracts of Reviews of Effects) found in Online Appendix 2 B. How to find the unpublished trials 1) Conference abstracts 2) Clinical trial registries and results databases 3) Contact trialists and sponsors 4) Industry trial registries and results databases C.
Background: With next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. Findings: Dedicated to 'whole-genome assembly-free' treatments, the Colib'read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory.
Overview The common denominator of all the tools presented is the fact that they are all dedicated to the analysis of NGS datasets without the need for any reference genome. An overview of these six tools is presented graphically in Fig. 1. Table  1  summarizes the inputs and outputs of each tool. In short, KISSPLICE, DISCOSNP, and TAKEABREAK perform de novo variant identification and quantification. For these tools the general approach is: 1) define a model for the elements sought; 2) detect in one or several NGS datasets those elements that fit the model; 3) output these together with a score and their genomic neighborhood. MAPSEMBLER2 generates a targeted assembly surrounding sequences of interest provided by the user. MAPSEM-BLER2 can provide the assembly as a graph and proposes a tool for visualizing it.
LORDEC uses short reads for correcting thirdgeneration long reads, and finally COMMET (COmpare Multiple METagenomes) is dedicated to the comparison of numerous metagenomic read sets. Special care was given to limit both the memory and time requirements of all tools. Thus, five of the six tools are based on the usage of a compact representation of a de Bruijn graph, as explained in the next section. Note that all the algorithms presented here were developed bearing in mind the need for simple and userfriendly tools. They can be applied on raw sequenced short reads, without requiring any pretreatment. However, if users are aware of bias such as contaminants or systematic sequencing errors, they can be used on preprocessed datasets and this can give better results.
Description of tools DISCOSNP DISCOSNP  [4]  is a reference-free SNP calling program that focuses on the detection of both heterozygous and homozygous isolated SNPs, from any number of sequencing datasets. The DISCOSNP method rests on the following observation: in the dBG, a SNP generates a pair of paths composed of k vertices, which represent 2k-1 length sequences that are polymorphic on one position. This corresponds to a so-called bubble in the dBG, as depicted in Fig. 2. The model formalism can be found in  [4]. DISCOSNP is composed of two modules: KISSNP2, followed by KISSREADS. The tool takes as input any number (potentially one) of read sets, i. e. samples. It processes all read sets together (creating the dBG and detecting the SNP-specific motifs) and outputs all isolated SNPs (for a given k) shared by any number of samples.
The KISSNP2 output is a multi-FASTA file in which every consecutive pair of sequences corresponds to the two paths of a SNP (2k-1 sequences) together with their left and right contigs, which are reconstructed with the MINIA assembler  [15]. The KISSREADS module maps back input reads on the sequences of the predicted SNPs in order to validate  them and to provide per allele coverage and per read set information. The coverage is then used to compute a phi score, i. e. a normalized chi-squared statistic varying between 0 and 1, which ranks best those SNPs that are discriminant between the samples. Finally, SNPs are sorted according to the phi score. DISCOSNP outperforms, mostly in terms of time and memory resources, state-of-the-art de novo or referencebased SNP discovery methods  [4]. Indeed, DISCOSNP scales remarkably well on big data studies as illustrated in Table  2.
KISSPLICE KISSPLICE  [2]  (Fig. 4 ) is a program that enables the analysis of RNA-seq data with or without a reference genome or transcriptome. It is an exact local transcriptome assembler that allows identification of SNPs, indels, and AS events. The software can deal with an arbitrary number of biological factors, and it is able to quantify each variant in each condition. KISSPLICE has been tested on Illumina datasets of up to 1 billion reads. The memory consumption is around 5 Gb for 100 million reads. The local aspect of KISSPLICE allows it to scale better to larger datasets than traditional global assemblers, for example Trinity  [17]. However, it does not reconstruct full-length transcripts, but only the variable regions. For instance, in an exon skipping event only the sequence of the skipped exon (plus some fixed-length context) is computed.
Variations in a transcriptome (including AS events) correspond to recognizable patterns in the dBG  [2] , known as 'bubbles' as briefly described in the DISCOSNP section above. An example of such a bubble is given in Fig. 3. The KISSPLICE program is composed of four steps: (i) de Bruijn graph construction, (ii) biconnected components decomposition, (iii) bubble enumeration, and (iv) event classification and quantification. In the first step, common to other Colib'read tools, the dBG is built from the set of reads using the GATB structure. The second step in KISSPLICE decomposes the dBG into biconnected components (see  [2]  for formal definitions). This step requires marking the nodes and cannot be performed with the current version of the GATB structure, explaining why an explicit representation of the dBG is required.
6. Such graphs are in JSON format. The visualization framework was designed to facilitate the interpretation of MAPSEMBLER2 outputs. Currently, this visualizer is compatible only with JSON format MAPSEMBLER2 outputs and with any dBG graph respecting the specific JSON characteristics. Further work will make GSV compatible with semantic web or systems biology tools to visualize, for example, RDF files or biological networks. COMMET COMMET  [6]  provides a global similarity overview between all datasets of a large metagenomic project. Directly from non-assembled reads, all-against-all comparisons are performed through an efficient indexing strategy. The results are stored as bit vectors, i. e. a compressed representation of read files, which can be used to further combine read subsets by common logical operations.
Finally, COMMET computes a clusterization of the metagenomic datasets, which is visualized through dendrograms and heatmaps. LORDEC LORDEC  [7]  is a tool to correct sequencing errors in long reads obtained from third-generation high-throughput sequencing technologies  [7]. Third-generation sequencing machines, especially PacBio, offer the advantage of delivering much longer reads than previous technologies (up to 20 Kb), often at the expense of sequence precision. Current estimates show that sequencing errors in PacBio reads average around 15 %, while traditional Illumina short reads exhibit an average error rate around 1 %. Moreover, PacBio sequencing suffers from a majority of insertion/deletion errors. It is thus necessary to correct these long reads before analysis, or at least during assembly, and different solutions have been proposed  [19] [20] [21] [22] , but these approaches "require high computational resources and long running times on a supercomputer even for bacterial genome datasets".