dataesr/scientific-paragraphs-categorization huggingface dataset:
c. Exclude intermediate levels of folders that do not convey any additional information. For example, consider a dataset having only TIFF files. Including an additional folder called tiff in the path <condition>/tiff/ files*. tif is redundant. By contrast, if the file format is important then <condition>/ <format1>/<files_of_format1> and <condition>/<format2>/<files_of_format2> is meaningful. d. Impose an upper limit on the number of files in a folder and if necessary split large directories so they do not contain more than a certain maximum number of files (e. g. , 10,000). If, for instance, a folder contains one million files then it could instead be organised as a folder (parent_folder) with 100 sub-folders (child00 to child99), each containing 10,000 files. This is important because different file systems have different tolerances for handling large numbers of files.
For example, the Second Extended Filesystem (ext2) imposes a 'soft' limits of 10,000 files per directory because of the extra overhead when processing such large folders (The Second Extended Filesystem -The Linux Kernel Documentation). While modern file systems are capable of handling larger numbers of files, the re-usability of the data will increase when taking into account systems with more modest resources, such as web browsers that may need to list or process all files in a directory. (4) Folder contents. a. Group related files unless it is instrumental to keep them separated. For example, group files by specimen, filetype, experimental purpose (treatment, control), etc. It may be crucial to separate different data types into different folders (e. g. , one for micrographs and one for particle stacks).
a. Restrict names to numerals and lowercase letters and replace all spaces with underscores or hyphens for meaningful word (group) boundaries, to make it substantially easier to work with the data. This facilitates easy transition between typing command line utilities or program names, which invariably work with lowercase (Windows PowerShell cmdlets are case insensitive even though they are documented in CamelCase e. g. Get-Command; similarly, macOS path names are case insensitive by default, though this depends on the chosen file-system formatting). Use underscores only for word boundaries and hyphens for keywords or other key attributes such as specimen names identifiable by the presence of a hyphen, e. g. , covid-19. Consistent use of case also improves readability  (Deissenboeck & Pizka, 2006).
Software availability To make our recommendations practical, we have developed bandbox  (Korir et al. , 2022) , an open-source commandline interface (CLI) tool to help users understand how they can improve the organisation of their data in preparation for archival. The program offers two CLI commands: view and analyse. Running bandbox view <dir> command displays a tree of a directory and all its contents; for every non-empty directory with files, bandbox provides a summary of the number of files in it, including a list of all the file formats encountered. Running bandbox analyse <dir> command provides a listing of possible issues grouped into categories in line with those specified in the Recommendations section. bandbox examines the tree associated with the nested hierarchy of files and folders in a dataset and then concurrently runs various heuristics on the tree which are controlled by configurations that the user may modify.
The results produced by the analyse command are only suggestions for improvement; we understand that there may be practical limitations to implementing some of the suggested improvements as well as good reasons for keeping the data as is. We have designed bandbox to be configurable and extensible allowing users to customise analysis parameters (file/folder name length, recognised file formats, accession names, regexes) as well as add new heuristics. An example configuration file is provided in the Github repository. Figures  3  and 4  show screenshots of the results of running bandbox on two different datasets. Software available from: https://pypi. org/project/bandbox Source code available from: https://github. com/emdb-empiar/bandbox Archived source code at time of publication: https://doi.
org/10. 5281/zenodo. 7807541  (Korir et al. , 2022). License: Apache License 2. 0 on the contributions of the article. There could be clearer differentiation of efforts to standardize bioimaging metadata (REMBI, QUAREP-LiMi), file formats and associated libraries (OME-TIFF, OME-NGFF, Zarr, n5), and local or cloud-based services that provide Data APIs (DVID, BossDB) with some level of abstraction in how data is actually stored. The recommendations and tool mainly apply to file-based solutions though some of the recommendations, such as naming, would be applicable to other forms of big data repositories. We suggest that the authors clarify the scope of their contributions. It should be noted that some of the efforts to standardize data and its distribution also have recommendations for organization of data.
For example, OME-NGFF requires segmentation to be in a directory called "labels/". In the third paragraph of Motivation, the terms "ways and means" and "organisational resources" are unclear though some of your examples (folder hierarchy, file formats, identifiers) show how data can be organized. We suggest you start with some examples and then introduce "organizational resources" as a term. If standardization is not an aim, can bandbox be configured to remove warnings not agreed upon by a user? In Figure  2 , the printing of the word "warning" for datasets with no red flags seems odd. We would suggest using "check" as in "name check" or "structure check" if no warnings exist. Given recommendation (8)b and the article's bioimaging focus, the bandbox tool should work by default with well-known, large-scale formats like Zarr and N5.
In testing, it appears that bandbox doesn't recognized file extensions used by such formats like. json and. zarr. The configurability of bandbox is a nice feature and should be mentioned in the article. This would allow other tool builders to contribute configurations for validating common formats and it seems like the regex capability could allow folder hierarchy requirements. The command-line bandbox tool should limit warning output to some maximum number of lines by default. This is particularly true for massive, chunked datasets consisting of many files and folders. We would suggest adding a "verbose" flag to allow full results to be output perhaps to a file.
Some minor points: The description of the bandbox tool could be moved out of the Motivation section and after listing the recommendations. Figure  1  has too small font sizes and would not be readable for printed copies as well as expending quite a bit of black ink. The phenomenon described in the first sub-bullet under 'Verbosity' is an interesting point that seems to deserve its own name. Maybe something like, 'redundant nesting' or 'over-nesting'. A name would also make it easier to connect to solution 3A, which is conceptually related. Also, the second half of this bullet point is in monospace font, but it should be Times New Roman or whatever. Some recommendations are more universally advisable than others. For those points, we'd recommend dropping "Consider" for stronger language. In (4)b, "Most archives allow multiple separate entries to be linked or grouped," it's not clear what qualifies as an archive since data could be made available through cloud providers' object stores and other facilities.
This is particularly true for massive, chunked datasets consisting of many files and folders. We would suggest adding a "verbose" flag to allow full results to be output perhaps to a file. This has been updated in bandbox v0. 2. 2. Instead of printing all results by default, we have substituted the -S/--summarise flag with a -a/--all flag so that by default users don't get overwhelmed. The instruction to use the new flag is now highlighted in yellow text beneath each section with more than a certain number of results.
Some minor points: The description of the bandbox tool could be moved out of the Motivation section and after listing the recommendations. We have now included a detailed description of bandbox in the Software Availability section. Figure  1  has too small font sizes and would not be readable for printed copies as well as expending quite a bit of black ink. We accept the suggestion and have changed all images to have a light background. The phenomenon described in the first sub-bullet under 'Verbosity' is an interesting point that seems to deserve its own name. Maybe something like, 'redundant nesting' or 'overnesting'. A name would also make it easier to connect to solution 3A, which is conceptually related. Also, the second half of this bullet point is in monospace font, but it should be Times New Roman or whatever.
○ The concept of "meaningful names" for folders raises questions about subjectivity and human sensitivity, which may not align with the precision required for effective data management structures. Establishing a clear naming convention, is objectively applicable across various contexts, would contribute to the robustness and reliability of the guide. Reviewer Expertise: Biology; image-based phenotypic profiling; microscopist; data generator; core facility management; FAIR metadata I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Response to Reviewer #3 (in italics) Specific comments: The guideline presented by Korir and colleagues, who are recognized experts in data structure, data organization, and FAIRification, marks an important step toward fostering a comprehensive discussion on the management of bioimaging data. The authors, primarily developers and bioinformaticians dealing with intricate datasets, have assembled a set of recommendations that, while valuable, may be perceived as overly abstract, potentially posing challenges for experimentalists who serve as the primary data producers. One critical aspect that emerges is the need for greater clarity regarding the intended audience for this guideline. Currently, it appears somewhat ambiguous, leading to potential misalignment with the individuals it should be primarily targeting.
○ 'Facet refers to the various attributes germane to the experiment which may be included in the folder and file names'. Should 'facet' not be called 'key'? If not then explain the differences between both terms. We used the term 'facet' in the same sense as in multifaceted, implying that a dataset may be viewed from various perspectives to discern distinct properties much in the same way as a gem. The reviewer's proposal of 'key' does not fit this sense.
" Advanced Light Microscopy, The Francis Crick Institute, London, England, UK 2 Advanced Light Microscopy, The Francis Crick Institute, London, England, UK The article is timely as we are facing a deluge of bioimaging data with higher resolutions and automation. It is therefore an area that needs more discussions, sharing of good practices. I mostly agree with all the recommendations given, although I feel that the authors may need to make a good argument for some recommendations. Some choices seem arbitrary and I would like to see the rationale behind them. After reading the paper, I am a bit confused by the article's intended target audience. Is the article recommendation aimed at most of the biologists who archive bioimaging data mainly for the purpose of peer review and references? Or is the article and recommendation aiming for those database curators and producers of bioimaging databases, e.
g. IDR ( https://idr. openmicroscopy. org/) , SSBD (https://ssbd. riken. jp/database/), GDC ( https://portal. gdc. cancer. gov/) , etc? On page 4 under the heading 'Motivation', "We believe that recommendations outlined here maybe of value to two principal groups of users: 1) data depositors, who need to design and prepare their data to improve its usability to the community". Does it include most biologists? I believe that most biologists archive their data to provide a record of their studies. Are the data depositors in the article and its intended audience refer to bioimaging database curators/producers instead of bench biologists? I believe that the ten recommendations would be equally applied to most biologists even though their aim is to provide a record of their studies, the recommendations would help those database curators to organise their bioimage data in more meaningful ways.
p11: The proposed path "data/brief_description/treatment3. " violates at least recommendations 3 (unused subfolder 'brief_description') and 7 (use of word 'data') As mentioned above, the example used here is purely illustrative and omits other content which would otherwise not violate this recommendation. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000. com.
Acknowledgements The authors are grateful to  Alex J. Noble  and  Christopher J. Peddie  for helpful feedback on the manuscript. We also gratefully acknowledge the many constructive suggestions from the reviewers:  S. H. W. Scheres ,  K. H. L. Ho ,  S. E. Le Dévédec ,  W. T. Katz  and  V. Scarlett. This work aligns with the recommendations of the EuroBioimaging/ELIXIR Joint Strategy (https://elixir-europe. org/system/files/euro-bioimaging_elixir_image_data_strategy. pdf), in particular the need for standards and approaches for the organisation of image data storage in established and emerging reference image domains. We acknowledge both ELIXIR and Euro-BioImaging's key roles in highlighting the importance of the effective organisation of biological image data. We have released an updated version (bandbox v0.
Study design and participants A global registry (COV-GN) was set up to collect clinical data of patients with COVID-19 and established or newly diagnosed glomerular disease, which presented to several nephrology departments worldwide during the early (pre-omicron) pandemic era from March 2020 until August 2021. The criteria for inclusion in this study were i) an established diagnosis of COVID-19, either verified via viral RNA detection or because of high clinical probability, e. g. by typical symptoms and contact to a verified case and/or characteristic radiologic findings on CT scan; ii) age ≥ 18 years at the time of COVID-19, and iii) a previously established diagnosis of a immune-mediated glomerular disease. Secondary forms of glomerular diseases and glomerular diseases that clearly are not immune-mediated (e.
Data collection Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Medical University Innsbruck  (13, 14). REDCap is a secure, web-based software platform designed to support data capture for research studies. An electronic case report form was established and transformed onto REDCap using a web-integrated project designer. Participating centers received access to an individual REDCap account. Sub-investigators screened local patient data for eligibility and entered pseudonymized patient records assigned to their respective center via their REDCap accounts. The online survey was launched on 29. 06. 2020 and closed on 31. 03. 2022. The following data were obtained: sex, age at diagnosis of COVID-19, height, weight, race, diagnosis of established glomerular disease, remission status (complete or partial remission, no remission, relapse) and immunosuppressive regimen (induction treatment, maintenance treatment, no immunosuppression) at time of COVID-19, clinical data on admission including O2 saturation and body temperature.
Laboratory data were collected on admission, peak values during follow-up and one month (3 to 5 weeks) after diagnosis of COVID-19 and included urea, creatinine, proteinuria, albuminuria, Creactive protein, serum albumin. Clinical outcomes of COVID-19 were assessed in categories (hospitalization, non-invasive ventilation, intensive care treatment, mechanical ventilation, kidney replacement therapy due to AKI, and mortality). Further information on prior treatment of glomerular disease included name of the medication, cumulative dose, treatment duration, a n d t h e t i m e i n t e r v a l f r o m l a s t a p p l i c a t i o n o f immunosuppressants used. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Medical University Innsbruck (1215/2020).
Data quality check (QC) and processing Detailed data exploration and processing were carried out to ensure that all unwanted data points (including negative data points and positive outliers) that could increase the percentage error bar were cleaned. Simple moving average was used to reduce data scatter. This is used to calculate the unweighted mean of the preceding K data points. The smoother the curve, the higher the value of K, but the less precise the result. We compute the simple moving average if the data points are p 1 , p 2 , …, p n. SMA k ¼ p nÀkþ1 þ p nÀkþ2 … þ p 2 k ¼ 1 k X n i¼nÀkþ1 p i (1) Two preliminary corrections were made on the data. (  1 ) Data cleaning: The datasets were loaded into python jupyter scripts as LAS extension files and presented in readable formats as Data-Frames.
Missing data points (Null of NaNs) were replaced with either the mean values, preceding or succeeding data points; assuming that the spread of the data points was similar due to close depth interval or same lithology. Outliers were completely removed, or a rolling mean average was used to have reasonable statistical distribution. (2) Accounting the depth of the seawater column: The initial depths were measured from the rotary table [meters below the rotary table (mbRT)] in the drilling platform as the reference point, in order to obtain the new borehole measured depth (MD) with the seafloor (mudline) depth as a reference level (meters below the seafloor). The seawater column was subtracted from the TD (total depths) in mbRT (MD  1 ). This is important because it helps to determine the exact pressure of the seafloor (P sea ¼ r w Z w g, where P sea is the pressure at the mudline, r w is the density of the seawater, Z w is the seawater column, and g is the acceleration due to gravity).
Overburden pressure Overburden stress or vertical stress (s v ) at a given depth is the total exerted pressure on a formation by the total weight of the overlying rocks and fluids above. The overburden stress was estimated using bulk density log data in Well-10 and Well-12, while in Well-05 the bulk density was derived from the porosity and grain density using: r b ¼ 4r w þ ð1 À 4Þr r (3) where r b is the bulk density, 4 is the porosity, r w is the density of water and r r is the density of the matrix. The overburden pressure is estimated by integrating the bulk density from the seafloor, considering seafloor pressure and the true vertical depth. The overburden gradient (sv g ) can be estimated using the equation below: sv g ¼ P sea þ ð Z 0 r b ðZÞgdZ ! À P sea Z ¼ ð Z 0 r b ðZÞdZ Z g (4) 3.
Feature scaling The nine well-log data were categorized as independent variable, while the pore pressure as the dependent variable. To preprocess the data, we performed feature scaling in order to normalize or standardize the independent variables or features into a finite range. There are several methods that can be used to achieve this: Absolute Maximum Scaling, Min-Max Scaling, Normalization, Standardization, and Robust Scaling. Here in this study the standardization method was used to calculate the z-value for every of the data points, hence, all features are centered around the mean value of zero and with one as the value of the standard deviation. Z s ¼ x À u s ( 11 ) where Z s is the Z-score, s is the score, u is the mean value, s is the standard deviation. For all three methods used here, the datasets were split into two: train datasets (80%) and test datasets (20%).
Conclusion In this article we predicted the spatial distribution of pore pressure in offshore Niger Delta by processing the well logs using python programming language. Our results have shown the evidence of overpressures with values increasing from the depth of 1110 mbsf (3038. 28 mbRT or 9968. 11 ft below rotary table) in Well-05, 868 mbsf (2873 mbRT or 9425. 853 ft below rotary table) at Well-10 and 1220 mbsf (2879 mbRT or 9445. 54 ft below rotary table) in Well-12. We have shown that pore pressure onsets is within the Agbada Formation and it is expected to increase to higher values in the Akata shales at deeper depths. Our results have provided an accurate representation of the pore pressure state in the offshore Niger Delta. We have shown that the use of Eaton's exponent in different parts of the Niger Delta needs to be applied with caution in order to properly depict the regional consolidation of the sediments.
, 2021; Nair et al. , 2021; Joshi et al. , 2020; Song et al. , 2020; Chen et al. , 2022). Regarding the summarization of hospital stays, some work  (Di Eugenio et al. , 2014; Acharya et al. , 2016)  used both physician discharge notes (free text) and the structured nursing documentation (such as nursing plans of care) to generate a unique summary. Others generated summaries from longform hospital admissions  (Adams et al. , 2023). Moreover, work has also been carried out on summarizing medical reports. For example,  Moramarco et al. (2021)  used the MTSamples dataset to fill automatically the 'description field' of a medical report based on the information present in the overall report. In addition, radiology report summarization  (Zhang et al. , 2020; Karn et al. , 2022)  is intended to produce a concise and easily comprehensible 'IMPRESSIONS' section from the rest of the radiology report.
Correct prediction of EHR categories could be useful for subsequent formal text generation. For utterances classification, there are different types of classification such as classifying noteworthy utterance  (Krishna et al. , 2021) , label prediction for medical conversation utterances  (Song et al. , 2020) , and dialogue turn classification  (Lacson et al. , 2006). In detail,  Krishna et al. (2021)  evaluated the multi-label classification of noteworthy utterances that are relevant to each summary section before clustering related utterances and generating one summary sentence per cluster. Their modular summarization technique outperforms its purely abstractive counterpart, producing much more factual and coherent sentences. Besides,  Song et al. (2020)  first identified two types of utterances (problem statements and treatment recommendations) and then generated summaries, they showed that for the particular dataset used, high-quality summaries can be generated by extracting these two types of utterances.
(2021). The two test simulations differ in their atmospheric forcing. In the first one, which we will call "RE-PEAT1970", UKESM1. 0-ice was run for several decades under constant 1970 greenhouse gas and other forcings. In the second one, which we will call "4xCO2", UKESM1. 0-ice was run for several decades under instantaneously quadrupled 1970 CO 2 concentrations. In our study, we use 60 years of simulation, from year 10 to year 70, for both runs. The training and the testing dataset result from NEMO simulations. Nevertheless, next to differences in forcing from the atmosphere and the ice and bed geometry, the training and testing ensembles also differ in several technical aspects of NEMO. The training simulations were run with the version of 4. 0. 4. of  NEMO (NEMO Team, 2019) , including the sea-ice model SI 3 , while the test simulations were run with the version 3.