dataesr/scientific-paragraphs-categorization huggingface dataset:
However, by the authors' own admission, the algorithm runs for a considerable length of time, and they themselves turn towards a multiple sample method. 19  With careful consideration of these issues, we present ifCNV, a novel machine learning-based software, provided as a python and R package (https://github. com/SimCab-CHU/ifCNV and https://github. com/SimCab-CHU/ifCNVR , respectively). It uses two IFs, one to split the CNV-positive (CNV pos ) samples from the CNV-negative (CNV neg ) ones, and a second to detect the altered targets within the CNV pos samples. This approach combines several advantages, among which is that it does not require an external nor internal reference and it is low resource-consuming. To validate our model and explore its limitations in clinical practice, we tested ifCNV on different synthetic datasets mimicking relevant clinical situations and on datasets obtained from amplicon-or capture-based DNA library preparation technologies.
Isolation forest algorithm The IF algorithm was developed by Liu et al. 30  It 'isolates' observations using a binary tree structure called an isolation tree. In this isolation tree, anomalies are more likely to be isolated closer to the root, whereas normal points are more likely to be isolated at the deeper end. The IF algorithm builds its isolation trees for a given dataset by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. The number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length is then averaged over a forest of such isolation trees to produce a decision value. The smaller the value, the more likely it is that the sample represents an anomaly.
Synthetic datasets To create synthetic datasets that reproduce faithfully those obtained in routine clinical practice, we selected 1910 samples from in-house targeted NGS data with no CNVs. We extracted the total reads on each target from the aligned. bam files with the bedtools  31  multicov function and created a reference reads matrix ordered with samples as columns and targets as rows. This reference reads matrix was then normalised by dividing each column by its median. All medians were used to create a median reads distribution that was needed for the rescaling process. Next, we created a normalised target reads distribution from each row of the normalised matrix. Thus, to generate synthetic datasets, we filled each line by taking a normalised target reads distribution, in which we randomly picked a value for each column.
Comparative genomic hybridisation aCGH profiling was performed with the Human Agilent Sureprint G3 8x60 K Microarray Kit (Agilent Technologies, Santa Clara, CA, USA). Tumour DNA was labelled with cyanine 5 (Cy5), while reference DNA from an individual of the same sex as the patient was labelled with cyanine 3 (Cy3). Sample and reference DNAs were pooled and hybridised for 24 hours at 67°C on the arrays. The fluorescence was read by an Agilent SureScan Microarray scanner and the Cy5/Cy3 fluorescence ratios were converted into log2 transformed values with Cytogenomics software (Agilent). The threshold of the absolute value of the log2 fluorescence ratio retained to define a chromosomal anomaly was 0. 25. A mean log2 ratio was calculated when, for at least three probes located on contiguous positions on the chromosome, a log2 ratio absolute value greater than 0.
25 and of the same sign was measured. The minimum size of the anomalies considered in the interpretation of the results was set at one megabase (Mb). The different chromosomal anomalies were defined by the Cytogenomics software according to the mean log2 ratio values, as follows: homozygous deletion for a value < -1, loss of one gene copy for a value between -0. 25 and -1, gain of one gene copy for a value between 0. 25 and 1 and amplification (gain of at least five copies) for a value > 2.
TruSeq Custom Amplicon library preparation assay -TSCA dataset Library preparation was performed as previously described. 33  Briefly, extracted DNA was qualified using KAPA Sybr® Fast qPCR (Kapa Biosystems, Boston, MA, USA). A home-made panel targeting specific exons of 35 clinically relevant cancer genes was used for amplification of regions of interest. For each sample, dual-strand libraries were prepared using a TruSeq Custom Amplicon protocol, as described by the manufacturer (Illumina, San Diego, CA, USA). After amplification, PCR products were purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA) and quantified, normalised and pair-end sequenced on a MiSeq instrument (2×150 cycles, Illumina). This dataset is composed of 81 samples from 59 different sequencing runs, with 25 CNV pos samples (Table  1 ).
Advanta Solid Tumor NGS library preparation assay -Juno dataset Libraries were prepared using the Advanta Solid Tumor NGS Library Prep Assay with the automated Juno ™ system on integrated fluidic circuits (LP 8. 8. 6 IFC) (Fluidigm, San Francisco, CA, USA) following the manufacturer's procedure. The panel is developed to allow the detection of somatic mutations in 53 oncology-relevant genes (234 kb, 1508 assays). Briefly, the LP 8. 8. 6 IFCs were primed with 20 ng of DNA per sample in the PCR mix. After amplification, pooled harvested samples were purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA), and a second PCR was performed to integrate the sequencing adapters. Libraries were then quantified, normalised and pair-end sequenced on a NextSeq instrument (2×150 cycles, Illumina).
Detection of CNV pos samples To quantify the ability of ifCNV to detect CNV pos samples, we created a synthetic dataset of 1500 targets and 30 samples, in which we inserted one CNV pos sample. It is noteworthy that if the copy number ratio (CNR) or the modified target ratio (MTR) are low, the CNV pos samples will resemble the CNV neg samples and therefore will be difficult to detect. Thus, the performance of a CNV detector directly depends on the CNR and the MTR. Taking this fact into consideration, we iterated the CNR and MTR (from 0 to 10 and 0 to 0. 1, respectively) and performed 1000 simulations for each iteration (figure  2 A, B ). The analysis of the attribution of CNV pos samples for a CNR greater than 1 is shown in figure  2A. For CNRs greater than 6, ifCNV correctly identified the abnormal sample in 99.
Identification of the CNV neg samples To quantify the ability of ifCNV to detect CNV neg samples, we used the same synthetic dataset, and we also iterated the CNR and MTR (from 0 to 10 and 0 to 0. 1, respectively) and performed 1000 simulations for each iteration (figure 2 C, D). ifCNV was able to correctly identify the CNV neg samples in 99. 87% of simulations, regardless of the CNR and MTR. For CNRs over 2, this reached 99. 9%. Interestingly, for CNRs under one, ifCNV identified the CNV neg samples in 98. 57% of the simulations. Finally, a value of 99. 69% was obtained for CNRs under 0. 5.
Detection of one CNV pos sample in a set of several CNV neg samples Conventional hospital and research laboratories must determine the CNV status of numerous samples. The number of samples in a sequencing run can vary from a few to several dozen. We therefore assessed the ability of ifCNV to correctly find a unique CNV pos sample in a set of several negative samples. We created synthetic datasets of 2 to 100 samples in which we inserted a CNV pos sample with a CNR of 5 and an MTR of 0. 03. We performed 100 simulations for each and calculated the sensitivities (Se), specificities (Sp) and accuracies (Acc) of the algorithm (figure  2E ). For one CNV pos sample in a set of two, ifCNV failed to label any sample as positive, leading to one TN and one FN (Se=0, Sp=1 and Acc=0. 5). Interestingly, for one CNV pos sample in three, ifCNV correctly labelled the positive sample in every simulation and found one FP in less than 50% of simulations.
Detection of multiple CNV pos samples in a set of several CNV neg samples As several CNV pos can be present in the same sequencing run, we tested the performance of ifCNV in such situations. We randomly chose 2 to 25 samples in a synthetic dataset of 50 samples. We then added random CNRs (from 2 to 6) to 3% of the targets of these samples and performed 100 iterations to determine the Se, Sp and Acc of our algorithm (figure  2F ). ifCNV exhibited relatively high Se, Sp and Acc (around 0. 85, 0. 7 and 0. 75, respectively), regardless of the number of CNV pos samples in the dataset. Notably, when half of the tested set (25/50) was CNV pos , ifCNV correctly labelled a mean of 20 samples across all simulations.
Detection of altered targets The second step of ifCNV consists of labelling the targets that are modified among the CNV pos samples. To assess its performance, we created a synthetic dataset of 30 samples and 300 targets. One sample was CNV pos with one modified target randomly chosen at each iteration, with a CNR from 0 to 5. We then performed 1000 iterations and calculated the Se, Acc, PPV and MCC (figure  3 ). On the one hand, ifCNV exhibited a Se very close to 1 for CNRs from both 0 to 0. 3 and 3 to 5, meaning that the modified target was accurately labelled in almost every simulation. On the other hand, the PPV and the MCC were low (~0. 02 and ~0. 1, respectively), reflecting a high number of FPs. However, the Acc was ~0. 87 and stayed approximately unchanged, meaning that the number of TNs was high and dominated the number of FPs.
Thresholding on the localisation score To test the ability of the score to discriminate the FPs from the TPs, we used the same synthetic dataset as before and grouped targets (from 2 to 15) together to mimic regions. We then modified all the targets corresponding to a randomly chosen region. Finally, we computed the localisation score and calculated the Se, Acc, PPV and MCC before (figure  4A , left panel) and after thresholding (figure  4A , right panel). As figure  3  shows, before thresholding the Se was close to 1 and the PPV and MCC were low (around 0. 1 and 0. 2, respectively). Acc was lower using the grouped targets (around 0. 4), because, by construction, the total number of regions in the dataset is lower than the number of targets and therefore the number of TN is smaller. After thresholding, we observed that the PPV, MCC and Acc increased to reach values very close to 1 for CNRs over 2, while the Se had only slightly decreased, meaning that the score thresholding enables the TPs to be kept while the FPs are discarded.
ICR96 dataset To compare the performance of ifCNV to that of other methods,  18, [24] [25] [26] 34  we compared our results to those obtained by Moreno-Cabrera et al. 35  who benchmarked five widely used tools with the ICR96 dataset. 32  This dataset, however, only possess one target per exon, rendering the advantage of the ifCNV score thresholding strategy not practicable in this case. As an alternative strategy, we decided to take advantage of the ability to change the contamination parameter of the IF. This main actionable parameter is the proportion of outliers in the dataset. By default, it can be set to a value between 0 and 0. 5 and to 'auto', for an automatic detection of the proportion of outliers. In ifCNV we added the ability to set this parameter to 'None'; it is then calculated as 1 on the number of samples in the dataset.
TSCA dataset Next, we aimed to validate the performance of ifCNV on an in-house dataset (Table  3 ). Its particularities are: (i) it is composed of tumour samples containing variable percentages of altered cells, and (ii) it possesses a small number of targets per region (range: 1 to 40, median: 4). Using this dataset, we found that ifCNV correctly labelled 19 of the 21 amplifications present in the dataset; the 2 FN were measured as a gain of two copies (CNR=2) by aCGH. In addition, 14 of 17 deletions were detected with no FPs. The 3 undetected deletions were from samples that have a lower percentage of tumour cells.
Juno dataset Finally, we also assessed our tool on a distinct library preparation approach with a larger number of targets per region composed of a larger panel than the TSCA dataset (range: 1 to 164, median: 17) from tumour samples (Table  3 ). Interestingly, we could detect all amplifications with no FPs and 17 out of the 19 deletions, leading to an overall Acc of 0. 96 and MCC of 0. 93. Interestingly, the 2 missed deletions are on a gene that represents only 0. 4% of the panel (MTR=0. 004).
Discussion In recent years, numerous computational methods for detecting and measuring CNVs from NGS data have been developed. However, most of these are based on the use of internal or external reference samples. To date, only a few have taken advantage of easy-to-use machine learning packages. 16, 17, 27  Here, we describe ifCNV, a bioinformatics tool using a machine learning-based algorithm, allowing detection of CNVs without the need for a reference sample. Moreover, in routine clinical practice, the variety of pathologies involving specific molecular alterations leads to a broad diversity in the datasets generated. Thus, in general, most CNV software that is developed for a specific data type has sub-optimal reliability for use in routine practice with diversified samples. In addition, most of the genetic workflows are either developed in python or R and, to our knowledge, no existing CNV detection tool is available in both languages.
ifCNV also performed well using datasets generated from amplicon-or capture-based libraries prepared from germline or somatic clinical samples. Analyses of real data also demonstrated that ifCNV's performance was comparable to that of other widely used tools,  35  but with substantial specific advantages. Our solution has a tuneable control of the FPR thanks to localisation score thresholding and to the contamination parameter, which can both be optimised according to the dataset, by an entry-level user. ifCNV was also able to accurately detect CNVs in difficult samples, such as those composed of a mixture of normal cells and tumoral cells, which dilutes the CNR of samples. In conclusion, ifCNV is a highly flexible tool that can detect CNVs in several types of clinical samples. ifCNV now represents an essential component of the cancer diagnosis pipeline that we routinely use to analyse samples from patients in our laboratory.
A'Campo and Baisch (2020) developed a tool for ultra-deep geothermal EGS projects. The entrance data are the classification of the project parameters such as gas content, mudweight or reservoir pressure from very high to very low values. A threat that can generate an event is then ranked according to this classification. From this, the likelihood of an event is estimated by multiplying the ranking of a threat by the relative contribution of this threat to the event. The advantage of this tool is that the risk is well defined from the characteristics of the site. However, a good technical knowledge is necessary to implement it and it is based on a small number of projects so further validation is needed even if changes are possible for advanced users to account for it. Another aspect of their method is that they separate the seismic risk from other risks, using an existing tool to rank seismic risk.
This tool: (i) provides a comprehensive-and editable-list of risks considered for all phases of a project; (ii) integrates different ranking levels as it is often project dependent (based on the level of knowledge); (iii) allows to realize several consecutive risk ranking for a same project with several plots; (iv) is downloadable as an excel sheet: https:// github. com/ Thoma sLeGu enan/ Geori sktool/ relea ses/ tag/ v2. 1. To add flexibility, instead of a list of risks valid for only one phase or a particular geological context, we chose to provide a global list of risks that can serve as a base for any type of geothermal project. Likewise, the ranking of the different risks and their consequences may depend on a project, so we opted for a user-dependent ranking. The ranking is semi-quantitative with score, but the tool also proposes a prototype of the risk register using probabilities with the aim to go towards a fully quantitative risk analysis.
The Triassic sandstones formation has had limited development for deep geothermal energy recovery so far but has proven to have important geothermal potential in the Paris area. The Lusitanian limestones have not been targeted so far, but were tested in some wells during drilling operations as the formation is crossed when targeting the Dogger. Since 1970, 199 wells have been drilled in the Paris Basin, with 180 targeting the Dogger limestone aquifer and 13 the Albian-Neocomian sands. In 2020, 85% of the 2 TWh of geothermal heat produced in France is produced for district heating networks in Paris area (from Sybase database presented in  Hamm et al. 2019b ). The Paris area has favorable conditions, subsurface geothermal resources plus important surface needs that (i) defining new priority development areas in other geological formations (e.
The extract, transform, and load (ETL) process is at the core of data warehousing architectures. As such, the success of data warehouse (DW) projects is essentially based on the proper modeling of the ETL process. As there is no standard model for the representation and design of this process, several researchers have made efforts to propose modeling methods based on different formalisms, such as unified modeling language (UML), ontology, model-driven architecture (MDA), model-driven development (MDD), and graphical flow, which includes business process model notation (BPMN), colored Petri nets (CPN), Yet Another Workflow Language (YAWL), CommonCube, entity modeling diagram (EMD), and so on. With the emergence of Big Data, despite the multitude of relevant approaches proposed for modeling the ETL process in classical environments, part of the community has been motivated to provide new data warehousing methods that support Big Data specifications.
In this paper, we present a summary of relevant works related to the modeling of data warehousing approaches, from classical ETL processes to ELT design approaches. A systematic literature review is conducted and a detailed set of comparison criteria are defined in order to allow the reader to better understand the evolution of these processes. Our study paints a complete picture of ETL modeling approaches, from their advent to the era of Big Data, while comparing their main characteristics. This study allows for the identification of the main challenges and issues related to the design of Big Data warehousing systems, mainly involving the lack of a generic design model for data collection, storage, processing, querying, and analysis.
Introduction The globalization and the spread of information technology, the strong concurrency between different companies, and the urge for quick and easy access to reliable and relevant information have incited business leaders to replace traditional business computing systems with other decision support systems. These business intelligence (BI) decision systems appeared with the introduction of the data warehouse (DW) by Bill Inmon in 1991. According to  [1] , "A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision-making process". A DW is a system used for integrating, storing, and processing data from often heterogeneous data sources, in order to provide decision-makers with a multi-dimensional view. The integration of these data is achieved through a three-phase process: extract, transform, and load (ETL).
This process is responsible for extracting data from different data sources, transforming them (by preparation, conversion, clean, filter, conversion, join, aggregation, and so on), and loading them into a DW. Consequently, the general framework for ETL processes consists of three sequential steps-extract (E), transform (T), and load (L)-and three main layers-data sources (DSs), data staging area (DSA), and DW  [2, 3]. First, the extract phase consists of extracting data from multiple sources and converting them into an appropriate format, which facilitates processing during the transformation phase. These DSs may be internal data sources, such as the enterprise database management systems (DBMS), XML files, flat files (text or. csv), and so on, or from external data, such as web applications, sensors, cameras, social media, and emails.
Thus, the associated databases, files, and so on are generally heterogeneous. Furthermore, according to  [4] , there are two logical methods and two physical methods for extracting data. More precisely, online and offline extraction are physical methods, while the logical methods are full extraction and incremental extraction. Full extraction is known as the "initial extraction"  [5] , and involves the first loading of the DW with data from operational sources. In incremental extraction, also called changed data capture (CDC), ETL processes refresh the DW with data modified and added-in source systems since the last extraction. This process is periodic, according to the refresh cycle and business needs. It also captures only data that have changed since the last extraction through the use of various techniques, such as audit columns, database log, system date, or delta technique  [5].
The transform phase consists of a set of transformations on the extracted data, in order to adapt them to a database dedicated to processing decisionmaking applications. These transformations are known as "common tasks" in  [6, 7] , or as "activities", according to other researchers  [8]. Among these activities, we find aggregation, join, conversion, and filter. These transformations are carried out in the DSA-the data preparation and cleaning area-before they are loaded into the target storage area; this is also known as intermediate data storage. Such physical storage contains all the temporary tables created during the extraction phase and the results of the transformation operations. Finally, the third phase of the ETL process is load, which transfers the transformed data to the target storage databases; that is, the DW.
The data that reach the final appropriate format are loaded into the DW according to a standard model, such as the snowflake schema or the star schema. The DW must respond precisely to the initial needs of the user. Nevertheless, in recent years, with the emergence of several new Internet services, web and mobile applications, and social media platforms (e. g. , Facebook, Twitter, Instagram), a new generation of data has emerged: Big Data. Thus, new challenges have been imposed, related to the large amount (or "volume") of received data, as well as the heterogeneity of these data, which can be structured, semi-structured, and unstructured (or "variety"), the time needed for their processing (or "velocity"), and, finally, the accuracy of the data, (or "veracity"). These terms represent the popular "4Vs" of Big Data in the literature  [9].
Such data exceed the ability of traditional tools to capture, store, process, and analyze data. Consequently, a new data warehousing strategy was proposed in this context, consisting of the extract, load, and transform (ELT) process  [10, 11]. Indeed, the data are extracted (1. extract) from the sources and stored (2. load) in their raw state in a data lake, applying only minor transformations (3. transform). Next, these data will undergo the necessary transformation tasks to be finally loaded in a target data warehouse. For more details on data lake management, we refer the reader to  [12]. In addition to the challenges related to the characteristics of Big Data, it has been confirmed that 80% of the time involved in the development of a DW project includes extracting, cleaning, and loading data  [13].