dataesr/scientific-paragraphs-categorization huggingface dataset:
Untargeted 1H-NMR Metabolomics We recorded spectra according to our NMR method as reported and previously applied  [35, 55]. Study samples, along with 146 Quality Control (QC) samples, were analyzed over 46 batches, whereas 99 Healthy Volunteer (HV) samples, were analyzed over 43 batches. A maximum of 15 samples per batch to limit intra-batch variability was selected, based on robust Principal Component Analysis (PCA)  [56]  results (R package "rospca"  [57] , version 1. 0. 4, Tom Reynkens), which showed QC samples outlying after 18 NMR experiments. NMR spectra recorded after this limit of 18 samples (n = 4, due to recording delays) were excluded. Samples were thawed and centrifuged at room temperature for the first three batches and at 4 • C for all the rest. A Bruker DRX AVANCE spectrometer equipped with a triple resonance inverse 5 mm probe head operating at 500.
13 MHz was employed for analyzing samples. Both the sequence and batch number of all samples were recorded for estimating technical variability. Longitudinal Eddy-Current Delay (LED) spectra were recorded and processed as previously described  [35, 55]. Spectra with high line width were not used for further analysis (maleic acid peak width > 1. 2 Hz, n = 4), along with spectra recorded from samples that had large chemical shift differences from other samples (n = 4) or peaks corresponding to EDTA (n = 2) or citrate (intensities corresponding to blood collection, n = 1). Four samples were retrospectively found to not correspond to any of the four disease groups (PA, PPGL, CS or PHT) and were excluded from the analysis. Areas corresponding to macromolecules, water, glucose and noise were excluded from data processing (areas above 10, 5.
35 to 5. 24, 5. 15 to 4. 40, 3. 91 to 3. 68, 3. 54 to 3. 36, 3. 26 to 3. 19, 1. 30 to 1. 10, 0. 90 to 0. 75, and below 0 ppm). R studio version 1. 1. 463 (J. J. Allaire, Boston, MA, USA)  [58]  running R version 3. 4. 4 (R core team, Vienna, Austria)  [59]  was used for loading the R package "batman"  [60]  version 1. 2. 1. 03 (Jie Hao), which was used to extract the spectra as tables. R package "SPEAQ"  [61]  version 2. 0. 0 (Charlie Beirnaert) was used for obtaining the peak table and was applied according to the script "SPEAQ pipeline 3". Due to their high number, SPEAQ was not possible to perform on all samples simultaneously. Hence, a script for aligning different SPEAQ batches was developed ("Combining1 2 PLASMA 3 new"). These two scripts can be found on the first author's GitHub page (https://github.
com/NickBliz/PPGL-PRE-VS-POST, last update on 5 June 2021) and data can be provided upon request. All subsequent data processing steps, which can be found in scripts on the first author's GitHub page (https://github. com/NickBliz/ENSAT-HT-PLASMA-NMR, last update on 28 September 2021), were performed on R version 3. 6. 3, loaded on R studio version 1. 2. 5033. The ethanol peaks at 3. 65 ppm, the highly variable histidine peaks at 7. 06 and 7. 81 ppm, the macromolecule peak at 2. 02 ppm and the peaks with a strong correlation with the run order of samples at 3. 185, 2. 21 and 1. 904 ppm were also excluded, along with peaks at 2. 26, 2. 28, 2. 31, 2. 37, 2. 39 and 4. 13 ppm, corresponding to ketone bodies. These latter metabolites were not taken into account as, giving rise to outliers, would not be valuable biomarkers.
HV samples could be separated based on the batch in which they were analyzed, and QC samples based on their run order within batches, but these analytical factors were not found to have a major impact on our dataset, as seen in Figure  S2. As the first step in data processing, features not present in at least 80% of samples belonging to either the QC or HV group  [62] , were removed. Probabilistic Quotient Normalization (PQN [63]) was applied as a normalization method, using as reference the median spectrum ignoring non-detects of a set of samples that were drawn from 98 HVs from the GYDR center. HV samples obtained from center FRPA1 were not used for normalization, as these only included healthy young male subjects. Next, peaks with a coefficient of variation of more than 30% in QC samples were removed.
Finally, after missing value estimation with the k-nearest neighbors (k-NN) method  [64]  (k = 10), using R package "impute" version 1. 58. 0 (Balasubramanian Narasimhan)  [65] , the generalized log transformation (GLOG  [66] ) based on 133 QC samples was applied using R package "LMGene" version 2. 40. 0 (Blythe Durbin-Johnson)  [67]. The QC samples were prepared by pooling plasma obtained from 390 anonymized plasma samples to a total volume of about 450 mL, which was subsequently aliquoted into 1 mL batches and stored at -80 • C until analysis. As a normalization and GLOG transformation optimization step, the process was repeated after the HV and QC sample groups were relieved of outliers detected by means of robust PCA  [56]. The resulting dataset was directly used for multivariate statistics.
We assigned peaks to metabolites using Chenomx evaluation v. 8. 4  [68]  and Bruker Topspin v. 4. 0. 6. We used the human metabolome database  [69] , along with the Madison Metabolomics Consortium Database  [70] , as references. To aid in peak assignment, additional methods were employed. Specifically, 2D NMR, namely correlation spectroscopy (COSY, cosyprqf) and J-resolved (JRES, jresgpprqf) pulse sequences allowed for investigating correlations between peaks and peak multiplicity, respectively. These experiments, along with 1D NMR on filtered samples in pH 2. 5  [71] , allowed for a number of peak identities to be confirmed, and some unknowns to be assigned to metabolites. COSY spectra were recorded using 4 K data points in F2 direction, 128 in F1, 16 scans, 8 dummy scans, a spectral width of 6 K Hz in both directions, an acquisition time of 0.
02 s in F2 and 0. 34 s in F1, resolution of 46. 89 Hz in F2 and 2. 83 Hz in F1. Receiver gain was set to the optimum value. Spectra were processed using Fourier transform in both directions and calibrated Trimethylsilylpropanoic acid (TSP) to 0 ppm. JRES spectra were recorded using 8 K data points in F2 direction, 64 in F1, 16 scans, 8 dummy scans, a spectral width of 8 K Hz in F2 and 60 in F1, an acquisition time of 0. 49 s in F2 and 1. 07 s in F1, resolution of 2. 03 Hz in F2 and 0. 94 Hz in F1. Receiver gain was set to the optimum value. Spectra were processed using Fourier transform in both directions, tilting and symmetrizing phase-sensitive spectra, and calibrated TSP to 0 ppm. F Furthermore, a spiking experiment was carried out, utilizing a QC sample and a non-related-to-the-study control sample, which were spiked with pyruvic acid, succinic acid, choline, carnitine, acetylcarnitine, methanol and 5-methylthioadenosine, along with histidine, serine, phenylalanine (Sigma) and tyrosine (Merck).
Data Analysis and Statistics Based on our research aims, we aimed to separate the following disease groups: (a) EHT (PA + CS + PPGL) vs. PHT, (b) PA vs. PHT (c) CS vs. PHT, (d) PPGL vs. PHT, (e) PA vs. CS vs. PPGL vs. PHT. Multivariate Analysis (MVA) was performed on meancentered data by employing the "MixOmics" R package version 6. 11. 33 (Kim-Anh Le Cao)  [72]  on R version 3. 6. 3, loaded on R studio version 1. 2. 5033. We used PCA  [73] , as an unsupervised method to discover trends and outliers within the data. Partial Least Squares Discriminant Analysis (PLSDA)  [74]  was used as a supervised method for establishing the signature for separating samples grouped by center as well as sample age (median sample age cutoff). Important PLSDA variables were determined based on their Variable Importance in the Projection (VIP) score [75] (median outer-loop VIP score above 1 after double cross-validation, or CV2) in PLSDA models.
Analysis of Variance Simultaneous Component Analysis (ASCA  [24] ) was used for removing the interaction between PCA cluster and disease group, present within the dataset (as observed in the PCA score plot of Figure  2 ). Sparse PLSDA (sPLSDA  [76, 77] ), was employed for investigating the metabolic signature (regression coefficients) of the differences between samples grouped by diagnostic category (PHT, PA, PPGL or CS, with EHT as a pool of the latter three and the first as controls). As an alternative to sPLSDA, l1-norm regularized logistic regression  [78]  was employed, using R package "glmnet" version 4. 0-2 (Trevor Hastie). Parameters for glmnet included alpha = 1, family = binomial and the minimum lambda, which was determined by cross-validation, was used for prediction, whereas "class" was selected as a prediction measure.
Finally, the left-out sample labels were predicted based on the model and using the Mahalanobis distance. Each model's signature was determined based on the total set of samples, after single CV and the same parameters as with CV2 (e. g. , 50 repeats). The maximum number of latent variables for (s)PLSDA models was 10, and for each inner cross-validation loop, the minimum number of components with the maximum accuracy was chosen, based on predictions using the Mahalanobis distance. All parameters and scripts for multivariate data analysis can be found on the first author's GitHub page (https://github. com/NickBliz/ENSAT-HT-PLASMA-NMR) and data can be provided upon request. Univariate statistical analysis methods were used to assess confounder effects on multivariate models and were performed using the "stats" R package  [59] , to check for data normality and to discover significant differences between variables.
Confounders Several approaches were used to investigate trends found by PCA: (A) Using ASCA  [24] , we estimated the confounder effects and used the Simultaneous Component Analysis residuals for sPLSDA. (B) We analyzed the data with PLSDA, based on groups defined by a specific confounder, to establish an inclusive list of all potentially relevant metabolites (i. e. , without searching for most predictive metabolites, but rather all related metabolites, in our attempts to prove the study "innocent" of confounding effects). These metabolites were thereafter excluded from the dataset, in the initial phase of data processing (i. e. , during the peak exclusion step), and the new dataset was used for classifying samples based on their disease groups. (C) Samples most related to the strongest confounder were removed, and classification models were calculated based on the remaining samples.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/metabo12080679/s1, Figure  S1 : PCA score plot of the complete dataset. This plot includes Healthy Volunteers (HV), Quality Controls (QC), as well as samples collected from all disease groups (PA, PHT, PPGL and CS). One patient was found to have adrenocortical carcinoma (ACC) and was left out of subsequent analyses. Principal components 1 and 2 were used for the plot. QC samples were technical replicates and were aliquoted from pooled plasma collected from patients not included in the present study. The spread of QC samples is indicative of technical variation associated to the data, which is significantly lower than biological.
5, 6 Recent studies emphasise that the availability of virus, patient, and therapeutic discovery data in a FAIR format could have accelerated the response to the Coronavirus 2019 (COVID-19) pandemic by enabling largescale analysis. 7, 8 Hence, FAIRifiying data and metadata is a prerequisite for attempts to elevate the overall reproducibility of research findings. 9. To realise the potential of FAIR research data management and to drive FAIR data adoption, several prominent collaborations between academia and industry in the life science discipline have been established. An example of FAIR implementation in this setting is the Pistoia Alliance (https:// www. pistoiaalliance. org), a precompetitive pharmaceutical industry collaboration that funds activities fostering FAIR adoption. 10 A second initiative is the FAIRplus project (https://fairplus-project.
eu), an ongoing EU project developing practical guidelines and tools to FAIRify clinical and translational biomedical data. FAIRplus develops FAIRification tools as part of collaborations with Innovative Medicine Initiative (IMI) projects seeking to FAIRify their data resources, with the aim that the tools can be reused by the wider community, therefore increasing their overall impact. 11 Both initiatives make a significant contribution to transforming data management and stewardship and drive FAIR implementation in the biomedical field. Recent studies have described FAIR implementation attempts in the pharmaceutical industry, which primarily focussed on improving the effectiveness of the drug R&D process  12, 13  and accelerating innovation within organisations. 14 Many pharmaceutical organisations are also understandably focussed on the associated costs and expected benefits of implementing these principles, in particular for retrospective processing of legacy data, where the immediate impact is arguably less clear than for ongoing projects.
FAIRification challenges Several hurdles might hinder the effective implementation of FAIR data principles. [17] [18] [19] [20] [21]  Financial challenges are related to the costs of the resources required to implement the FAIRification process, beginning with establishing and maintaining physical data infrastructures. They also include the significant costs of employing personnel and providing for the long-term sustainability of the data resources. 15, 18 By contrast, technical challenges are associated with the infrastructure, tools, and methodologies that are required to perform FAIRification (with the help of persistent identifier services, metadata registries, ontology services, etc. ). 19 egal challenges correspond to requirements that might pertain to the processing and sharing of the data (e.
g. , accessibility rights and compliance with data protection regulations), both for meeting the 'accessibility' and 'reusability' criteria as well as for performing the FAIRification process itself. 20, 21 Organisational challenges include providing training to the individuals who would implement and maintain the FAIRification processes. Furthermore, these also involve the development and sustaining of an organisational culture that elevates and rewards the practice of FAIR research data management (https://rdmkit. elixir-europe. org/). 13 All the above-mentioned challenges must be systematically addressed to implement FAIR effectively and apply equally to both retrospective and prospective processing of data sets. We define 'data set' as a collection of related records, of the same type, presented in a structured way.
This typically includes individual files, sets of web pages, or spreadsheets. Table  1  lists the challenges that affect FAIRification along with their required expertise. Although some criteria used for the selection of data undergoing FAIRification can be opaque and inconsistent, the legal and ethical aspects of decision-making must take into account protection of participants' data, respecting rights and freedoms as well as ensuring the interests of data owners. For example, Boeckhout et al. stated that ethical and legal aspects significantly affect the implementation of FAIR practices for sensitive human data. 17 imilarly, Holub et al. identified guidelines to help compliance with legal requirements for FAIR data in health and medical research. 21 At the outset, if personal data are involved, a thorough assessment of the access and reuse conditions of the data should be made and the requirements for compliance with General Data Protection Regulation (GDPR) and/or other applicable data protection legislation should be satisfied to ensure that the FAIRness goals do not contradict data protection principles.
22, 23  general data protection procedure, based upon the requirements of the GDPR framework,  24  should be formulated covering aspects such as data usage, storage, and the intended purpose of analysis when personal data are involved. If the data set to be FAIRified contains sensitive personal data, such as health or racial or ethnic information, for which data protection regulations specify a stricter framework for processing,  25  then an assessment of the suitability for FAIRification should identify the security and confidentiality requirements that must be fulfilled as part of the FAIRification process. 21. A Data Protection Impact Assessment (DPIA) should also be conducted to evaluate the risks of data processing and define the measures to take to address those risks and demonstrate compliance with data protection regulation.
26 In situations in which anonymisation of data is not possible, participants' consent should be sought and security measures (e. g. , authentication procedure, rules for access, tracking of access, data encryption, etc. ) should be considered to protect the privacy of individuals. FAIR Research Data Management (FAIR RDM) procedures are aligned with the necessary compliances needed when working with sensitive data, for example, access in FAIR is meant to apply only for appropriate individuals. However, it can be burdensome in some cases and recent calls have been made to refine GDPR regulations to better account for emerging reuse scenarios for sensitive data sets. 27. The status of a data set, including the quality and completeness of metadata, is a crucial factor influencing decisionmaking on implementing FAIRification, because this defines the intrinsic suitability for reuse.
However, in many cases, it is not possible to predict the full range of reuse scenarios for a specific data set while designing the FAIRification objectives. 28 It might also be the case that the scale of data involved could still be insufficient or statistically underpowered to answer meaningful scientific questions. 29 Therefore, there may be a lower threshold of the size or scope of a FAIR data set, depending on its reusability needs, such as rare diseases research. For example, the requirements to achieve statistical power in an analysis do not change just because the underlying data set has been FAIRified. Similarly, upper thresholds might exist because of the resources needed to perform FAIRification, especially if the FAIRification procedure is not scalable or readily automatable. 19.
Furthermore, it is crucial to clearly define the underlying goal of the FAIRification effort, especially when it relates to 'nontechnical' factors, such as meeting contractual obligations to funders or complying with the data management policy of an organisation. 31 Recently, extensive guidelines have been established on the implementation of FAIR-based Data Management Plans (DMP) by the European Commission as well as national research funding organisations. 32 he benefits that pharmaceutical organisations stand to gain from data-resource FAIRification are linked to the basic business imperative to bring safe and effective products to patients in a timely and cost-efficient manner. For this group, when considering which resources to FAIRify, the greatest potential impact will come from data associated with the complex and expensive clinical trial processes.
33, 34 To convince management to invest in relatively complex FAIR working, especially with sensitive patient data, more examples are needed that show clearly how using community-derived FAIR data has an impact on real-world problems, such as selecting the best treatment regimen or trial design for a specific patient cohort. 13, 35 In both drug discovery and development, FAIR data management procedures will potentiate the adoption of improved analysis methods, including ML and AI-based tools and workflows. 36, 37 Thus, users will be able to generate, test, and validate general prediction models and/or processes in their specific data domain. 6 The higher aggregation levels achievable in this way will pave the way to more-precise models of human health and disease at the molecular, cellular, tissue, and organismal levels.
The value and potential of the reuse of a FAIRified data set differ according to the perspective of the individual scientist or organisation. Personal factors are important for individual scientists who naturally seek to elevate the findability of their own research results, thereby increasing their citation rates, bringing benefits in terms of improved scientific recognition and career progression. 14 For project teams, FAIR data management might allow for a study to progress more efficiently because fewer experiments might be necessary when FAIR community-sourced data can be substituted for planned experiments, thereby reducing unnecessary repetition of studies. 15, 21, 28 The expected cumulative impact of access to FAIRified data is to achieve a higher degree of process reproducibility because of better metadata descriptions, which can reveal the source of inconsistencies between studies.
It is crucial for the success of FAIR implementation to evaluate the above-mentioned challenges, along with the value generated from FAIRification based on the principles of the cost-benefit analysis. 38 Such an analysis is a powerful way to support decision-making by evaluating the associated costs and expected benefits in a complex situation. 39, 40 A recent study introduced the FAIRification costs and the FAIRification benefits in the pharmaceutical R&D setting. 15 It identified a set of factors that affect the costs and values of the FAIRification process to make an effective decision on data prioritisation. The cost factors refer to the set of indicators or aspects that influence the costs associated with the FAIRification process. These factors are: (i) the legal and ethical considerations, which include the access rights and the ethical compliance; (ii) the technical resources, which pertain to the availability of the IT applications needed to perform the FAIRification and the availability of the documentation, such as DMP; and (iii) the human resources, which mean having skilled personnel to carry out the FAIRification.
By contrast, value factors can be defined as the value proposition for performing the FAIRification. These factors are: (i) the societal value, which concerns addressing an area of priority need or crosscutting impact; and (ii) the scientific value, which focusses on the uniqueness and novelty of the data set, its potential synergies, covering of a domain, and the domain expert availability. Table  2  summarises the identified cost and value factors that might influence decision-making on the FAIRification process. The absence of a systematic methodology for deciding whether a biomedical data set requires FAIRification led the FAIRplus consortium to develop a series of identification, evaluation, and prioritisation procedures. Selection is achieved by assessing several dimensions: (i) current status of the data and resources needed for FAIR implementation; (ii) ensuring that FAIRification goals meet intended use and reuse scenarios; and (iii) any scientific and socioeconomic impacts arising from FAIRification.
Methodology for data prioritisation for FAIRification To prioritise biomedical research data for FAIRification, robust identification and evaluation procedures were developed based on practical experience gained from working across 15 IMI projects covering discovery to clinical stages (Figure  1 ). Given that investment in FAIRification must also support its own strategic goals, FAIRplus prioritised collaboration opportunities with IMI projects that would lead to a high scientific or societal impact. This approach also enables representation of the challenges faced by the wider community and, hence, these learnings could be codified in the FAIR cookbook and generally applied. Phase 1: Project and data set information collection The information collection phase was carried out in two main steps.