dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-cell RNA-Seq data analysis CellRanger software (v2. 1. 0) was used to demultiplex the raw data, generate quality metrics, and generate per-gene count data for each cell. Data was then imported into the R statistical environment (v3. 5. 2) as SingleCellExperiment objects. Scater (v1. 8. 0) R package was used for data pre-processing and quality filtering. A total of 31,026 cells across the 6 FL + 4 rLN samples, or ~3,000 cells per sample were recovered after data pre-processing and used for analysis. Count data was log-normalized and Scran (v1. 9. 11) R package was used for batch correction (fast mutual nearest neighbors, MNN). The matrix containing corrected low-dimensional coordinates for each cell was used for downstream analyses. For visualizing these data in UMAP, we used the pl.
Bulk RNA-Seq data analysis Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2. 5. 2. a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis  57. Gene-level read counts were quantified using HTSEQ-count (v0. 11. 0; intersection-strict, reverse mode)  58. Genes showing low read counts (i. e. , genes not showing counts per million (cpm) > 1. 0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3. 22. 2) package  59. Differential gene expression analysis was performed in R (v4.
1. 1) using the DESeq2 package (v1. 34). Batch correction was performed to account for sample source (cell suspension vs. snap frozen whole tissue) using the removeBatchEffect function in limma (v3. 50. 3). Differentially expressed genes were filtered for absolute log2 fold-change > 1 and Benjamini-Hochberg-corrected p-value <0. 05 (2-sided Wald test). Heatmaps were generated using the pheatmap package (v1. 0. 12).
Targeted sequencing We used the TruSeq Custom Amplicon assay (TSCA; mean coverage: 767; range: 128-2,039; SD: 180) to identify variants within the protein coding regions of 59 genes commonly mutated in human B-cell lymphomas (Supplementary Data 7)  [60] [61] [62]. TSCA variants were validated with the Fluidigm Access Array system which achieved a 97% validation rate. Discrepancies between TSCA and Fluidigm results were further validated by Sanger sequencing.
IGHV mutation status assessment To identify the immunoglobulin heavy-chain variable (IGHV) mutation status from bulk RNA-seq data, we developed an in-house pipeline  63  motivated by the work of Balchly et al  64. Briefly, we performed de novo assembly of the paired-end RNA-seq reads using Trinity (v2. 1. 1)  65 to construct IGHV transcripts. We selected the most abundant and productive IGHV transcript to define somatic hypermutation status. The selected transcript sequence was queried against NCBI IgBLAST (v1. 14. 0) to identify IGH-V, D, and J genes. IgBLAST was used with default parameters to detect percent identity between the query and the highest similarity germline IGHV gene.
Acknowledgements This work was supported by operating grants from the  Cancer Research Society  (Montreal; to A. P. W. ) and  Canadian Institutes for Health Research (CIHR ; to A. P. W. ), a  Program Project Grant  from the  Terry Fox Research Institute  (TFRI; to A. P. W. , S. P. S. ,  C. St , and D. W. S. ), Large Scale Applied Research Project funding from  Genome Canada ,  Genome BC , and  CIHR  (to  C. St  and D. W. S. ), and infrastructure support from the  BC Cancer Foundation.
This data is part of an ongoing study, and is also available under restricted access. Access can be requested as above. Genome alignments were performed against the reference human genome assembly GRCh37/hg19 [https://www. ncbi. nlm. nih. gov/data-hub/genome/GCF_000001405. 13 /]. Exon junction coordinates were referenced from GENCODE release 19 [https://www. gencodegenes. org/human/release_19. html]. Single nucleotide polymorphisms were identified using dbSNP build 137 [https://www. ncbi. nlm. nih. gov/projects/SNP/snp_summary. cgi?view+summary=view +summars&build_id=137].
A public database was used which includes images of COVID-19 patients, patients with viral pneumonia, patients with pulmonary opacity, and healthy patients. The methodology used in this study is based on transfer learning of pre-trained networks to alleviate the complexity of calculation. In particular, three different types of convolutional neural networks, namely, InceptionV3, ResNet50 and Xception, and the Vision Transformer are implemented. Experimental results show that the Vision Transformer outperforms convolutional architectures with a test accuracy of 99. 3% vs 85. 58% for ResNet50 (best among CNNs). Moreover, it is able to correctly distinguish among four different classes of chest X-ray images, whereas similar works only stop at three categories at most. The high accuracy of this computer-assisted diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.
In this context, networks are able to acquire knowledge from pre-trained networks on large-scale image datasets or alternative data-rich sources. The classification algorithm based on transfer learning acquired results with an accuracy of 97. 66% and an F1-score of 97. 61%  [9]. The studies suggested that transfer learning can allow the network to extract significant features related to the COVID-19 disease diagnosis. In fact, several works have applied this idea in order to rapidly develop a reliable tool to assist medical experts in diagnosing COVID-19. The wide popularity of convolutional neural networks made them the first choice for a number of works, in which said architectures manage to identify COVID-struck lungs on X-ray images. Shazia et al. [10]  compared performances of several CNNs, presenting a test accuracy of 99.
III. DATABASE DESCRIPTION: CHEST X-RAY IMAGES The dataset used in this work is a collection of chest X-ray images that were (and still are) gathered by researchers from different countries, with the specific purpose of creating a publicly available database for COVID-related research. The version used for this work was collected in October 2021, and it consists of 3616 COVID-19 positive cases, 10,192 normal images, 6012 pulmonary opacity (non-COVID lung infection), and 1345 viral pneumonia  [18]   [19]. All images were downloaded as png-formatted RGB images with a size of 299x299x3. The lungs are the two organs responsible for supplying oxygen to the body and for the elimination of carbon dioxide from the blood, or the gaseous exchanges between air and blood (a process known as hematosis).
Embedded patches are arranged into a sequence and then fed to the Encoder, which exploits the multi-head attention technique to extract information, patterns and relationships among image patches. Eventually, outputs are fed to a Multi-Layer Perceptron to perform classification. In order to better clarify the similarities and differences between CNNs and ViT, a brief of the architectures of three among the most relevant and popular Convolutional Networks will follow. InceptionV3 originated as a module for GoogLeNet  [24] , with the purpose of allowing for deeper networks without increasing too much the number of parameters. 1x1 convolution blocks were introduced to reduce dimensionality. 1x1 convolutional layers act as rectified linear activators as well, so their purpose is two-fold. The next CNN chosen for analysis and comparison is Xception  [25] , which was described as an "extreme" version of InceptionV3 with the exploitation of the so-called depthwise separable convolution, and a subsequent redefinition of the Inception module.
D. EXPERIMENTAL SETUP AND TESTS The first step of the process consisted in training and testing the three aforementioned CNNs over the chest X-ray dataset. All of the networks were trained by exploiting the transfer learning technique, which allows to retain and freeze network weights derived from previous training sessions over specific datasets. In this case, weights obtained over the ImageNet21k database were used. Subsequently, only some of the top layer weights were set to be trainable. Indeed, the differences among the chosen architectures caused the number of trainable layers to change from one neural network to another. The reduced number of unfrozen layer weights were trained and tested over the chest X-Ray database. This method allowed to significantly reduce the overall amount of time and computational resources dedicated to the training and testing phases for all convolutional networks.
Data augmentation with random horizontal flipping and random rotation (set to 0. 2), as well as Mitchell-Netravali  [18]  filtering were applied to the database images before feeding them to the CNNs. On the other hand, no operation of any kind was performed before feeding the images to the Vision Transformer, with the exception of resizing them from an initial resolution of 299x299x3 to 224x224x3 in order to fit the ViT input layer.
This often leads to situations in which the network excels at performing its tasks on a given dataset but is unable to generalize over different scenarios. This becomes particularly significant when the primary purpose of the algorithm is to provide a fast and reliable response when assisting physicians in clinical diagnosis. For this reasons, future work in this direction should be dedicated to shed some light on what might lead a deep neural network into making a specific choice when facing alternatives, trying to bring clarity into what has traditionally been perceived as a black box. For instance, an interesting path to follow could be represented by the kind of ideas described by Hassani et al. [29] , who tried to exploit the convolutional networks' capabilities to extract significant features from images and feed those information to a Transformer, as opposed to using the patching and embedding method.
COMPARISON BETWEEN VISION TRANSFORMER AND CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES ResNet50 exhibits the best performance among the convolutional neural networks of choice, with a test accuracy of about 86%. However, the Vision Transformer architecture clearly outperforms the selected CNNs on this specific image classification task with an outstanding accuracy of 99. 3%. A few more indicators are shown in Table II and Fig. 7, 8, 9 and 10 to further describe the performance of the ViT architecture over the four classes of the database: precision, recall, F1-score, a visualization of the attention map and the confusion matrix, all of which are metrics and parameters commonly used to assess the performance of deep learning architectures over given taskslike image classification. The Support column in Table II refers to the number of images per category that were used to test the Vision Transformer ability to assign images to each category.
Scientific discovery can be aided when data are shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data  (Wilkinson et al. , 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i. e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project.
INTRODUCTION A common desire for Earth Science laboratories in the computer age is the digital storage and archiving of datasets in searchable databases. Furthermore, a growing number of funding agencies and publication venues are mandating that datasets are deposited in an open repository, so that other researchers may have access to the data. The 'big data' benefits of such a system for palaeoceanography are clear; data from multiple locations and periods of the Earth's history can be searched, sorted and presented according to, for example, proxy and/or species type. Such an approach would save significant person hours currently spent by researchers worldwide in searching for, downloading, understanding and digitising datasets, thus allowing for much more efficient analysis of data. The principles guiding this process are the principles of findability, accessibility, interoperability, reusability (FAIR)  (Wilkinson et al.
, 2016). Much of the discussion involving the establishment of standardised digitised data has revolved around defining an ideal database format and/or repository for the storage of data  (Bolliet et al. , 2016; Jonkers et al. , 2020; Khider et al. , 2019; McKay and Emile-Geay, 2016) , which is indeed a key prerequisite for the ultimate end goal whereby all data is stored on a common, publicly searchable/queryable online database in line with the goals of FAIR data. However, an often overlooked primary step in the realisation of such an end goal is ensuring that palaeoclimate data produced within a laboratory and/or research group is stored in some kind of machine readable format from the beginning, i. e. during the creation step. Current practices at many laboratories involve multiple actors and researchers of various levels of computer proficiency saving their data using idiosyncratic and machine-unreadable file formats.
These practices lead to increased workload both during the project and also at the end of the project when submitting data to online repositories (i. e. due to laborious post-hoc data formatting and extensive manual metadata entry at the time of submission). Paradoxically, striving to store data in the perfect format at the final step can impede widespread sharing of machine readable data, as the increased workload can actually discourage a laboratory from producing digitised, shareable data in the first place. Within the ERC ACCLIMATE project at Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, we digitised data from many existing records and created a simple GUI viewer with which to view them. We found that some trade-offs were necessary in order to digitise as much data as possible as efficiently as possible and in an internally consistent and machine readable way.
We considered existing database formats, but found that, at least in the case of our project, striving for the most complete data format with very rich metadata was a time consuming process for our purposes. Our primary consideration was to take concrete steps to promote and ensure early adoption and awareness of the machine readable ethos within the project and laboratory (i. e. upon the generation of the data), by creating a machine readable format that worked for all participants within our project and contained the necessary metadata needed for our work, thus enabling us to produce as much digitised data as possible, with the metadata that we required. We note that when developing any system, there are always trade-offs involved; do researchers want a lot of data with essential metadata, or less data but with very extensive metadata? This is a philosophical question and the answer depends on the particular needs of a project.
Given the aforementioned requirements for our particular project, we determined that the ideal data file format should, in our case, meet the following three criteria: 1. the stored data must be machine-readable across many operating systems, thus allowing for automated reading of data, as well as bulk conversion/uploading to common database formats; 2. it must be human-friendly, thus allowing the human eye to quickly access and understand the data contained within the file if needed; not all project participants have sufficient proficiency with higher level storage formats such as SQL, NetCDF and/or JSON. 3. the file creation process must be as accessible as possible and cause as little labour burden as possible for laboratory members of all levels of computer proficiency, thus encouraging the seamless and autonomous creation of machine-readable data formats from the very beginning of the project workflow (e.
g. , in the field or at the time of laboratory analysis), preferably offline without having to navigate some kind of data entry form and/or web portal. Here, we present a file naming and file structure format known as the Palaeoceanography ARchivIng System (PARIS), which is optimised for human-accessibility from the very beginning of a project (in this case, the stable isotope laboratory environment). Files stored in such a machine-readable file structure can subsequently easily be automatically batch-converted to the specific format requirements of a particular data repository, thus avoiding repeated manual metadata entry upon repository submission. We also demonstrate the machine-readable power of this simple file format as a basis for a simplified database structure to use within a laboratory: we have built a fully documented GUI in Matlab for interactive searching and plotting of data using our simple file  Lougheed et al.
Open Quaternary DOI: 10. 5334/oq. 101 format. We use Matlab for the GUI because it is what we had at hand, but the PARIS database format itself is language agnostic and could be exploited using any other programming language environment. The GUI allows for the rapid searching and visual presentation of data by latitude, longitude, water depth, age and, where applicable, species type. The entire setup was designed with modular expansion in mind, and both the file formatting conventions and GUI can be used and/or modified by other laboratories for their own particular needs. The structure of the archiving system is shown in Figure  1  and described in the following sections.
FILE STRUCTURE AND ORGANISATION FILE NAMING CONVENTIONS We use a file storage system based on universally readable, tab-delimited ASCII text files, which are more than sufficient for palaeoclimate datasets from sediment cores, seeing as such sediment cores contain discretedepth measurements numbering only in the hundreds or thousands. We chose tab delimitation over comma delimitation to avoid issues in locales where a comma is used to denote the decimal marker (e. g. much of Africa, Europe and South America). These tab-delimited files can easily be created directly from analytical software or by using basic spreadsheet software. A uniform file naming convention is used to create machine readable identifiers containing information about the data contained within the file: core name, data type (six character code) and measured material (e.
INTERNAL FILE STRUCTURES Raw data files A common challenge preventing long-term data sharing in palaeoceanography is the publication of isotope data exclusively vs age, which prevents re-evaluation of the data by future researchers as understanding of geochronological methods improves and evolves. For these reasons, all isotope and other palaeoclimate proxy data in the PARIS scheme are stored against core depth as the primary format, allowing for the later application of new geochronologies, and/or comparison of proxy data vs multiple geochronologies. A further ambiguity commonplace in palaeoceanography is reporting only a single core depth value corresponding to a particular data point (for example, often only a single core depth value is given, even though subsamples represent a depth interval).
Age-depth model files Within the PARIS system, separate age-depth model files are used to assign age and age uncertainty to the raw data that is stored against depth. Age-depth model files (corename_admodel. txt) are contained in a folder called "master" within the "age models" folder. The reason for this additional subdirectory level is to allow different age model scenarios to be stored, which can subsequently be accessed from the GUI. For example, one might wish to store and compare different age-depth models based on different methods ( 14 C, U/Th, etc) for the same set of sediment cores. Similarly, one may want to compare age-depth models developed by different software packages  (Blaauw and Christen, 2011; Bronk Ramsey, 1995; Haslett and Parnell, 2008; Lougheed and Obrochta, 2019). In that case, an additional folder can be made within the "age models" folder, and its contents will be accessible from the GUI.
Core information index file All raw data files and age-depth model files contain a unique code detailing the sediment core that they come from. An additional file (_core information. txt) is present within the main folder of PARIS, which details some basic meta-data for each core, namely location (latitude and longitude) and water depth (mbsl). This allows the PARIS system to subsequently search for sediment core locations that match a specific search criteria (e. g. a certain water depth or latitude/longitude bounding box) and search for all raw data and age-depth models associated with sediment cores that correspond to the search criteria.
GUI SEARCH INTERFACE To demonstrate the power of the text file based archiving system, and in order to provide a system with which laboratory members at LSCE could browse and visualise sediment core data, a GUI system was developed in Matlab (Figure  2 ). This system allows the user to search for sediment core locations according to certain criteria, and specify which types of data to plot, which are shown on three vertical panels (Figure  3 ). Data from multiple sediment cores and/or species can be plotted on to one of the first two panels, in order to facilitate inter-core comparison. Data can be plotted against depth or against age (from one of the supplied age-depth models), and the user can choose to plot with or without error bars. The third panel is reserved for plotting reference data or sediment accumulation rate (SAR) plots.
DATABASE INTER-COMPATIBILITY POTENTIAL Once all data from a given laboratory is stored using a common format, the process of submission to a given    database or repository (i. e. changing the format to suit a particular repository) can be fully automated. One needs only to write a one-off batch script that can convert all files from the laboratory to the required format of the various repositories. Here, we provide an example of a similar such script (dataonage. m) that was used within the LSCE laboratory to systematically read in all isotope data vs depth and output all isotope data vs age according to their respective age-depth models. Hence, systematically updating the age values when (re-)submitting to repositories becomes a simple and rapid task.
CONCLUSION The palaeoclimate literature has begun to embrace the principles of FAIR data and many good examples of useful database structures have been previously provided  (Bolliet et al. , 2016; Jonkers et al. , 2020; Khider et al. , 2019; McKay and Emile-Geay, 2016). We have provided an example of a concrete first step in the journey towards FAIR data, the creation of machinereadable data at the field and laboratory level. The involvement of multiple actors within a project requires that a machine readable format is fully accessible to persons with only basic computer proficiency. The simple PARIS file naming and structuring system, based on the ubiqutously accessible tabbed-text file format is one such example, and has been successfully deployed at LSCE. The simple structure is nonetheless powerful in that data can easily be indexed and searched, as demonstrated here using a GUI for data visualisation.
ACKNOWLEDGEMENTS This is a contribution to the  ACCLIMATE ERC  project; the research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013 Grant agreement n°  339108 ). B. C. Lougheed acknowledges  Swedish Research Council (Vetenskapsrådet)  grants  637-2014-499  and  2018-04992.