dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work Dependency theory has been an important subject of database theory for 575 more than twenty years. Several types of dependencies have been proposed, capturing different semantics useful for different such as query optimization, normalization, data cleaning, error detection, among others. Indeed, functional dependencies in their basic definition are not suitable for several tasks and data settings. As such a myriad of generalizations have been 580 proposed over the last twenty years which were recently and exhaustively reviewed by Caruccio et. al  [5]. Their classification separates (i) extent relaxations where FDs are relaxed on their coverage or conditions (e. g. the FD holds in a subset of data) from (ii) attribute-comparison relaxations where the notion of agreement between two objects for a given attribute is revisited.
185 datasets [ 27 ] 27. Given a FD X → Y and a tuple t, the value of t[X] determines the value of t[Y ]. With neighborhood dependencies, the value of t[Y ] is predicted not only by the value of t[X] but also by the neighbor values of t[X]. Let T be a set of tuples and x ∈ U. A closeness function on the attribute x is a function θ x : T [x] × T [x] → [0, 1] that computes how near are two values of 190 the attribute x in a scale [0, 1], where the closer to 1 the function evaluates for two values, the more similar they are (and vice-versa). Obviously, θ x (a, a) = 1.
4) The proposed context enables the user to prescribe a maximal detection delay and a false alarm probability for a given number of observations, and can know which change amplitudes can be detected with guaranteed minimal probability. One can note that the present submission is an extended version of the conference paper  [9]. In comparison to this prior publication, this paper includes a statistical study that allows the calculation of an upper bound of false detection probability and a lower bound of power function. The sharpness of those theoretical findings is verified on a large dataset. This paper also includes a practical study on the impact of the parameters of the proposed methodology in order to select the most relevant ones. The present paper is organized as follows. Section II briefly recalls the well-known cumulative sum (CUSUM) procedure  [10]  and states the problem of change-point detection for a non-stationary process emphasizing on the main difficulties and limitations of the CUSUM in this context.
To conduct these experiments, a data base of 500 000 successive healthy images has been acquired using an area scan camera installed over the production line of a wheel factory. The acquired images are made of 2046×2046 pixels of 12 bits depth. The procedure described in section IV has been applied to obtain the observations m i with i = {1, 2,. , 500 000}. The observed standard deviation, related to the imaging system, is σ = 22. As supposed in section IV, this parameter is assumed to be constant during the monitoring process. However, the variance can be changed with the acquisition conditions, for instance, with the illumination intensity. To deal with the problem of imaging acquisition system drift, the variance is periodically computed (typically at the beginning of each week). The detection problem of abrupt changes in acquisition conditions is not addressed in this paper.
In 2014, Getz and colleagues of the Broad Institute estimated that to discover all potentially actionable mutations that occur at a prevalence of approximately ≥1%, approximately 5,000 samples per tumor type would need to be sequenced and analyzed. 2 On the basis of this article and his experiences as AACR president and Global Alliance for Genomics and Health steering committee member and with The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international data-sharing consortium focused on enabling advances in precision oncology through the gathering and sharing of tumor genetic sequencing data linked with clinical data. The project's history, operational structure, lessons learned, and institutional perspectives on participation in the data-sharing consortium are reviewed.
Individuals involved with the inception and execution of AACR Project GENIE from each member institution described their experiences and lessons learned. The consortium was conceived in January 2014 and publicly released its first data set in January 2017, which consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. Commitment and contributions from many individuals at AACR and the member institutions were crucial to the consortium's success. These individuals filled leadership, project management, informatics, data curation, contracts, ethics, and security roles. Many lessons were learned during the first 3 years of the consortium, including on how to gather, harmonize, and share data; how to make decisions and foster collaboration; and how to set the stage for continued participation and expansion of the consortium.
INTRODUCTION The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international genomic data-sharing consortium focused on enabling advances in the understanding and treatment of cancer. The consortium's first data release was on January 5, 2017, and an article that explored the scientific rationale for the project, the data standardization process, the landscape of the first publicly released data set, and future challenges was published. 1 Now, the consortium has moved on to the work of preparing its next data releases, deciding which data fields to add, and reviewing applications for new members. With that work in mind, this review highlights some of the practical and administrative aspects of starting and running the GENIE consortium from the perspectives of AACR and the initial eight member institutions.
We hope that the lessons shared here will assist new GENIE members and others who embark on the journey of forming a genomic data-sharing consortium. the clinical sequencing program at Memorial Sloan Kettering Cancer Center, Charles L. Sawyers, MD, proposed the concept for AACR Project GENIE. He championed the premise that no single institution would be able to sequence a sufficient number of patients to improve clinical decision making in all tumor types, particularly in rare cancers and for rare variants in common cancers, and that the solution was to pool data from multiple institutions and share it publicly. In June 2014, AACR convened a think tank to gauge interest and begin to define the structure of what would become AACR Project GENIE ( Fig 1). A key early decision was to focus on genomic data from high-quality clinical laboratories to increase reliability of mutation calls, not require reanalysis by a centralized institution, and result in a much greater number of patients with matching genomic clinical information than from research efforts.
ADMINISTRATION Participating Institutions AACR Project GENIE is executed through a coordinating center that comprised a director, clinical data manager, project manager, and program coordinator. The coordinating center staff is crucial to ensuring that the project is fully operational. Eight founding data-contributing institutions were chosen to provide a manageable number of institutions that could provide sufficient data to answer a clinical question within 2 years. Of note, these institutions had ongoing CLIA/ISO (Clinical Laboratory Improvement Amendments/International Organization for Standardization)-certified clinical sequencing programs in 2014 as well as expertise in the clinical and bioinformatics challenges of converting clinical data into knowledge. Furthermore, the founding institutions expressed a willingness to share clinical cancer genomic data and longitudinal outcomes and had institutional support to do so.
A requirement of membership in the GENIE consortium is that each institution contributes at least 500 genomic records annually. The genomic data are accompanied by a minimum set of required clinical data elements. All institutions signed a master participation agreement and data use agreement before data submission. All institutions are required to protect and maintain patient privacy, which generally was done through existing consent for sharing deidentified data, specific consents for sharing specifically to GENIE, or a waiver of consent from the local institutional review board (IRB). 1  In addition, all institutions are required to meet data standards and timelines and to participate in meetings and on committees. The founding institutions of AACR Project GENIE are the Dana-Farber Cancer Institute (Boston, MA); Institut Gustave Roussy (Paris-Villejuif, France); has been partially established to provide guidance to the steering committee on pediatric oncology and to serve as a liaison between GENIE and the pediatric oncology community.
Meetings Communication has been essential to the GENIE consortium's success, and meetings are a critical communication vehicle. The steering committee and other groups previously described meet regularly through Web conferencing. Twice a year, the steering committee and individuals from each participating institution meet at an in-person summit to discuss progress and plans. In addition to the operational and scientific value of these approximately 40-person meetings, the summits help to strengthen the consortium through shared activities and meals, and they encourage the generation of new ideas and formation of new collaborations. In addition to the summits, ad hoc in-person meetings occasionally are required. One example was the data "hackathon" where a group assembled to finalize the data and begin the manuscript.
Expansion of the Consortium After the first public data release, an open call for new sites was broadly publicized, including direct communication with each institution that had inquired about joining. The application process was handled through an online survey that opened March 8, 2017, and closed May 1, 2017. Any institution that began an application before the close date or requested an extension was granted an extension to May 15, 2017. The survey results were reviewed by the participation subcommittee by using criteria published on the GENIE Web site. 4 Any unresolved issues or follow-up questions were handled on an applicant-by-applicant basis. Finalists were then given instructions to complete a sample data upload to identify potential issues in interacting with the project data platforms.
DATA MANAGEMENT Several open-source technologies allow GENIE to share and analyze data safely and securely both internally and externally (Fig  3 ). The Synapse platform from Sage Bionetworks (Seattle, WA; www. synapse. org) provides data versioning and provenance and serves as the primary data hub for the consortium. 5 For sponsored studies, clinical data are collected and staged by using the open-source Research Electronic Data Capture (REDCap) system 6 (www. project-redcap. org); these data are subsequently imported into Synapse. All data are pushed into cBioPortal  7, 8  (www. cbioportal. org/genie) for data visualization and analysis; data also can be directly exported from both Synapse and REDCap for analysis in other platforms. In addition, each technology permits multiple levels of access control so that public, consortium, and sponsor access to the appropriate data sets is easily managed on all platforms; data access is centrally managed by Sage Bionetworks.
Genomic Data All genomic data are mapped to ref 37 (hg19). Note that the Genomic Data Commons (GDC) uses GRCh38, and we likely need to begin to transition GENIE in this direction. The consortium only shares variant calls and not raw data, which was one of the early agreements that facilitated data sharing. For each gene panel, a browser extensible data (BED) file is provided, which is a list of all the genomic features that a particular test sequences. Each site defines a workflow file that links information about the sequencing assay to the BED file. The BED and workflow files may not be straightforward to obtain if an institution uses outside vendors for its sequencing and if these file types are not included in the data use agreements between institutions and vendors. After the initial data were available to the consortium, an ad hoc working group analyzed the initial data freeze for the manuscript and first public release.
As part of this exercise, a few initial issues were identified, including mutations in cis (which occurs when two variants in proximity are reported with similar allele fraction), adherence to variant call format, normalization of gene symbols, and the need to develop a germline filter for tumor-only sequencing. 1  The filter removes as many private or noncommon single-nucleotide polymorphisms as possible. Although the filter is effective, we could not be sure that all risk of patient reidentification was removed; thus, we implemented terms of use that must be reviewed and endorsed before users can access data. Currently, only CLIA/ISO-certified data are eligible for submission to GENIE. The guidelines for sequencing varied somewhat between institutions,  3  and this is summarized in Table  1.
Submitted data are rejected if they fail validation with tools developed collaboratively between Sage Bionetworks and scientists at participating institutions. In addition, although the central GENIE database hosted by Sage Bionetworks contains all mutation data reported by each center, cBioPortal currently excludes silent, intronic, 3′ untranslated region, 3′ flank, 5′ untranslated region, 5′ flank, and intergenic region mutations; however, these will be made available in cBio-Portal in the near future.
Data Redistribution The consortium is deeply committed to open science and data sharing as long as patient privacy is protected. To this end, terms of access have been implemented that require steering committee approval of any data redistribution, which is predicated on the ability of the receiving platform to implement the same terms of access that GENIE has implemented. To date, five requests for redistribution have been received and granted. As good global citizens, the consortium has deposited its first data release, version 1. 0. 1, in the National Cancer Institute GDC. 9  Our interactions with GDC were straightforward, and because of the submission it has already received from Foundation Medicine, GDC is prepared to accept a subset of clinical data and to help with mapping. The most difficult aspect of the submission was the database of genotype phenotype registration.
Data Access and Sharing Several tiers of data access are built into the master participation agreement. After GENIE is fully operationalized, institutions will have sole access to their own data for 6 months from the date of the sequencing report. Then, on a twiceyearly basis, genomic and clinical data will be deposited for all samples sequenced 6 months ago. Consortium members will have access to the data for 6 months, after which the data will be released to the public. For the first public data release, GENIE members waived their consortium-only access. The initial public release of GENIE data occurred on January 5, 2017, and consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. The data came from 12 unique next-generation sequencing panels that ranged from 46 to 429 genes and covered 1,040 unique genes.
Of note, the most commonly mutated genes in the population are TP53, KRAS, and PIK3CA, respectively, with CDKN2A and CDKN2B deletions and CCND1 amplification representing the most common copy number alterations. The three most common cancers are non-small-cell lung cancer, breast cancer, and colorectal cancer, respectively, with lung adenocarcinoma, invasive ductal carcinoma, and colon adenocarcinoma comprising the most common cancer subtypes (Fig  4 ). The public can access the data either through the GENIE-specific instance of cBioPortal or download the individual data sets for manipulation as the user sees fit directly from Sage Bionetworks. 10 s of September 5, 2017, 2,608 individuals had requested access through cBioPortal, and up to 557 individuals had downloaded one of the files from Synapse (Appendix Table  A2 ).
F G F 1 9 ( 1 1 q 1 3. 1 ). and work to pull and process data submitted to GENIE. This section describes the institutional perspectives of participation in the GENIE consortium. F G F 4 ( 1 1 q 1 3. 3 ). F G F 3 ( 1 1 q 1 3 ). F G F R 1 ( 8 p 1 1. 2 3 -p 1 1. 2.
Institutions remain in the consortium because they value the cross-institutional pollination and commitment to public data sharing as members of the "community of the willing. " Participation in GENIE has been a stimulating and instructive experience where lessons learned have been immediately applied to improve internal processes. Furthermore, researchers at many institutions anticipate opportunities to conduct research on the basis of GENIE data and look forward to taking advantage of the institutionand consortium-only access built into future submissions and releases of data sets.
Institutional Stakeholders Each institution noted the many individuals involved in the project who played a range of roles, including leadership, project management, informatics, contracts, IRB membership, security, pathology, clinical services, clinical genetics, and data curation. All underscore the requirement for commitment from institutional leadership to participate; however, buy-in from the clinicians and researchers who generate and want to use the data is essential. There is a need to engage this group to address concerns before sharing data. The institutions have taken different approaches, but those who have used an internal point person (or site lead) as the primary point of contact between the GENIE coordinating center and other internal stakeholders reported this as an effective way of managing the consortium relationship.
Numerous ethical, legal, and logistic challenges are associated with international data transfer. One of these concerned the invalidation by the European Court of Justice of the safe harbor procedure for exchange of personal data in 2015, which means that alternative procedures had to be implemented to allow the European partners to continue their participation in GENIE. Furthermore, the consortium encountered intellectual property issues related to clinical trial-associated data, such as disclosure of treatment with investigational agents. Discussion and consideration of these hurdles early and a consensus on data elements to be collected and how to handle potential intellectual property and PHI concerns are important. Consortium members believe that the data will have a far greater impact on the cancer community if made publically available.
Furthermore, AACR believes strongly that all AACR members should have access to these data, which is achieved through public release. However, public release of data requires additional safeguards for patient privacy, especially when genetics data are included. Given that most of the GENIE members' next-generation sequencing panels tested tumor tissue only and not tumor-normal pairs, special processing was required to remove potential germline alterations in somatic tumor calls to decrease the risk of reidentification in the public data set. We hope that GENIE's approach to public release of data will serve as a model to other consortia.
DISCUSSION In conclusion, the AACR Project GENIE member institutions are excited to share their first public data release and look forward to continued participation in the consortium. Several lessons were learned related to creating and sustaining a data-sharing consortium, which are described here; Appendix Table A3 lists all the obstacles encountered. First, the move from project initiation to first data release took longer than anticipated, and the ability for the steering committee to deviate from the original contract and adapt to changing needs was important for the success of the project. Second, the sharing of genomic data was fairly easy and straightforward because of a high concordance of data definitions and standards and a structured electronic form from their generation, unlike much clinical data.
ACKNOWLEDGMENT We thank  Nicole Peters  at the  American Association for Cancer Research (AACR)  for help with figures and  Kristin Dang  at  Sage Bionetworks  for usage data. Biobanking and BioMolecular Resources Research Infrastructure The Netherlands provided support in establishing alternative solutions after invalidation of the safe harbor procedure. The content of this article does not necessarily represent the official opinions of the AACR or the member institutions of  AACR  Project  GENIE.
To address this important gap in the current computational modeling software infrastructure, here we introduce Arkheia. Arkheia is a web-based open science platform for computational models in systems neuroscience. It provides an automatic, interactive, graphical presentation of simulation results, experimental protocols, and interactive exploration of parameter searches, in a web browser-based application. Arkheia is focused on automatic presentation of these resources with minimal manual input from users. Arkheia is written in a modular fashion with a focus on future development of the platform. The platform is designed in an open manner, with a clearly defined and separated API for database access, so that any project can write its own backend translating its data into the Arkheia database format.
e. , 3 and 4). 3. Explicit formalization of experimental protocols and neural data analysis allowing for (a) automatic testing and comparison of the models, (b) their efficient communication and reuse, and (c) deep introspection of the results 4. Tools that can utilize 1, 2, and 3 to automatically communicate and serve all aspect of the modeler's workflow to the rest of the community and public to facilitate collaboration and outreach. Recently we have made advances in addressing some aspect of points 1, 2, and 3 by the release of the Mozaik toolkit  (Antolík and Davison, 2013) , which allows us here to start approaching the limitation 4, by introducing Arkheia 1. Arkheia is a web-based platform for data management and communication of computational modeling outcomes in systems neuroscience.
It provides an automatic, interactive, graphical presentation of simulation results, the experimental protocols used, and interactive exploration of parameter searches, via a web browserbased application. Arkheia is focused on automatic serving of these resources with minimal (virtually no) manual input from users. Arkheia is written in modular fashion with a focus on future development of the platform. It follows the standard database-server-client design and is based around modern widely adopted web-based technologies (MongoDB for the database, Node. js and Express. js for the server, and AngularJS for the client). Currently, Arkheia is shipped only with a Mozaik backend, as at present this is the only published framework providing sufficient introspection of simulated neural data that can be automatically harvested for the presentation in Arkheia.