dataesr/scientific-paragraphs-categorization huggingface dataset:
) in PeakForest retrieved from each analytical system. With the referencing of these analytical data in PeakForest, hypaphorine (or lenticin) proved to be also detected in human urine  (Garcia-Aloy et al. , 2020)  and could be annotated with a confidence "level 2" with spectra stored in the MoNA public database. 15 Additionally, signals obtained with NMR spectra prediction of hypaphorine could be searched for in the experimental NMR profiles of the same biological sample (NIST plasma) in order to confirm its identity (confidence level 2) and enrich the NMR set of data of biological compound in PeakForest. Eventually, when available, the authentic standard may be purchased, analysed and reported in PeakForest, to reach "level 1" annotation.
Discussion Improving confidence in the outputs of data annotation and metabolite identification in biological samples is a priority for the metabolomic field  (Wishart et al. , 2022). Here we present the first comprehensive web-based infrastructure which can simultaneously handle MS and NMR data, and is designed to organize spectral data for metabolome annotation. PeakForest is able to capture and store experimental spectral data (peak lists) as well as associated annotations, and offers the possibility to curate information over time, to be browsed and reused in data treatment and, ultimately, shared across laboratories. PeakForest has the added value of providing a structured system with onsite data centralization and security for small laboratories with limited IT support. Over the last 15 years, a number of database structures and tools have been proposed to generate local data repositories for either MS or NMR spectral collections, promoting accuracy in metabolite identification  (Ferry-Dumazet et al.
, 2011; Horai et al. , 2010; Palmer et al. , 2018). PeakForest builds on the principles of these tools, and incorporates the latest developments in web frameworks and applications to provide a unique data model compatible with multiple research questions, depending on laboratory focus and/ or instruments. In addition to storing metabolites/spectral metadata, our data model can describe information on sample preparation, biological species identifiers and biological matrices, providing a complete compound profile for future use. For example, a dedicated PeakForest database can be implemented for a particular disease or class of biological species, based on concepts of "sample type-specific databases". Indeed it has been shown that the use of a restricted search domain increases the precision of annotations  (Reisdorph et al.
, 2021). By centralizing a large diversity of metabolites and profiles, PeakForest proposes a well-adapted database for studies based on a multi-platform untargeted strategy. Studies in metabolomics are increasingly using highthroughput screening technologies and large sample batches; these approaches generate "big data" and bring new challenges regarding data management and security. The key to efficient data management involves setting up appropriate data stewardship during production, treatment, mining and knowledge dissemination. In this context, global guidelines are now available to help specific metabolomics communities to enhance their data usage and new knowledge creation  (Savoi et al. , 2021). However, applying these recommendations can be complex and often requires changes in traditional work practices, new skills and/or training  (Griffin et al.
, 2019). PeakForest adopts FAIR practices  (Johnson & Lange, 2015; Wilkinson et al. , 2016)  in order to ensure that data at the laboratory level is consistent with, and can be used by, the international metabolomics community. PeakForest uses standardized vocabulary, including accurate and relevant attributes. Metabolomics annotation recommendations have been implemented, such as assigning cross reference ID on metabolites and making sharable reference spectra  (Kind et al. , 2018; Redestig et al. , 2010) , and use of unambiguous and persistent identifiers for metabolites and LC-MS data  (Wohlgemuth et al. , 2016)  and will facilitate the future integration of such data in data contextualization tools  (Cottret et al. , 2018; Delmas et al. , 2021). Interoperability with data analysis and data mining tools is ensured by open communication protocols (REST), and data export functions with conversion into common open standards formats ensure interoperability with academic databases and open repositories in the metabolomics community.
Overall, the PeakForest framework is designed to encourage greater openness of laboratory databases, facilitating integration into public databases and knowledge sharing between laboratories, and ultimately promoting effective metabolomics research (fewer redundancies across laboratories, faster annotation of unknowns). PeakForest has been extensively beta-tested by the members of the French metabolomics community. It is fullyoperational (available at https:// peakf orest. org/), and already currently used in research laboratories in France and abroad. In order to minimize server storage of unnecessary data, PeakForest is designed for metabolite annotation report data rather than storage of raw spectral data. Technical installation of PeakForest in any laboratory information system requires personnel with computing or bioinformatics skills, but once installed, the interface is user-friendly and the infrastructure is adapted for use by MS and NMR scientists with no specific computer skills.
Of course, a minimum number of compounds and spectra need to be imported by users in order to make local PeakForest databases fully-functional. In addition, PeakForest does not include data clean-up or extraction tools per se and requires metadata to be manually entered in the database; users supply completely-cleaned data following SOPs edited by the PeakForest team, and PeakForest tutorials are available to assist in this step. Data and metadata within PeakForest can be changed (curated) with ease; traceability of requested modifications is ensured via curation modules. This feature is ideally-suited to local data facilities under the supervision of a data manager, instrumental experts or associated scientists, but is less easy to deploy in larger-scale public repositories as it requires centralised and standardized highly-reactive error detection and correction processes.
As with all databases, initial construction of the relevant database and spectral collection in line with laboratory needs will take some time. Deployment of PeakForest is an iterative process, and initial investment in the database will yield increasing returns in terms of accuracy and efficiency as the spectral collections are incremented. At present a number of basic peak-matching tools are available as add-ons via the Galaxy toolshed, but additional developments are needed to enhance the MS-MS and NMR-2D matching capabilities, such as interoperability with MS-Dial  (Tsugawa et al. , 2015). For laboratories with access to bioinformatics support, integration of bespoke tools to the PeakForest infrastructure can contribute the optimization of local data pipelines analysis and metabolite annotation.
Conclusion This paper describes a novel digital infrastructure for the development of "new generation", structured and interoperable databases. PeakForest is intended to overcome a technical bottleneck, namely large-scale collaborative spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. The PeakForest database is a user-friendly data management solution, which can be used to build metabolite and spectral collections, and has in-built modules which allow users to curate and mine annotation mass and NMR data via Web interfaces and external tools. By generating an ecosystem of interoperable databases, PeakForest represents a significant advance for promoting open science in the field of metabolomics, maintaining and promoting good practice and scientific rigour, as well as increasing the shareability and findability of data.
Acknowledgements The  PeakForest  project is supported by the  French National Facility in Metabolomics & Fluxomics, MetaboHUB  ( 11-INBS-0010 ), launched by the  French Ministry of Research and Higher Education and the French ANR  funding agency within the  Programme "Investissements d'Avenir ". The authors thank all MetaboHUB nodes and French metabolomics facilities for their investment in the development project and their collaboration in spectral collection, production and curation. We also thank  Professor D. Rolin  for his continual support of the project and its team,  Dr. Y. Guitton  for critical reading and  Dr. J. M. G. Bloor  for significant improvements to the manuscript.
Data availability This paper does not present data but a digital solution. A PeakForest demonstrator is available at https:// demo. peakf orest. org. Software availability Demo: https:// demo. peakf orest. org/ webapp/; Project Web portal: https:// peakf orest. org/; Code repository (Major releases): https:// github. com/ peakf orest; Docker files: https:// hub. docker. com/u/ metab ohub.
Study Design We conducted a user-centered design and participatory research study to develop, qualitatively assess (usability and acceptability), and deploy a social media web application (interactive website) called the COVID-19 Social Site  [14, 15]. We curated and customized the site for LTCWs with low COVID-19 vaccine confidence within a broader randomized clinical trial (ClinicalTrials. gov, NCT05168800), funded by the Patient-Centered Outcomes Research Institute (COVID-2021C2-13181). We reported results using the Consolidated Criteria for Reporting Qualitative Research (CORE-Q) and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)  [16, 17].
Acknowledgments Research reported in this manuscript was funded through a  Patient-Centered Outcomes Research Institute  ( PCORI) Award  ( COVID-2021C2-13181 ). The statements presented in this manuscript are solely the responsibility of the authors and do not necessarily represent the views of the  PCORI , or its Board of Governors or Methodology Committee. We acknowledge the broader CONFIDENT study team's support and advice, particularly our long-term care worker partners. We thank the following collaborators:  Feng Chen ,  Patrick Delva ,  Jon Edwards ,  Branden Fillbrook ,  Tammy McIlnay ,  Katie Page ,  Lupita Rodriguez ,  Rowena Sheppard ,  Mary White  and  Celeste Wooten.
Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones.
However, these works did not deal with the indoor thermal comfort improvement when observing a thermal discomfort. This may be possible especially with the emergence of IoT networks. Moreover, to the best of our knowledge, no work has studied how outdoor environmental variables may affect Fanger's indoor parameters in order to provide indoor thermal comfort. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then propose to predict the indoor Fanger's parameters by considering the outdoor parameters, rather than predicting the complicated PMV equation value. To do so, we generated a multi-output prediction model using the random forest algorithm  [12]  based on a longitudinal real dataset of one year  [13].
Machine Learning: How to Model the Indoor Thermal Comfort Parameters Prediction? We consider long-term data including both outdoor parameters such as outdoor ambient temperature, outdoor humidity, outdoor air velocity, etc. , and their corresponding indoor influencing parameters on occupant's thermal comfort such as air temperature, air relative humidity, air velocity, etc. The dataset comprises m representative samples (rows) and h features (columns), and is divided into two sets: the training (or learning) set, to train and create the model, and the testing set, to evaluate the thermal comfort prediction. We used a supervised machine learning algorithm which takes both input and output (desired) data to learn a mapping function, F(x), from an h-dimensional input vector, x = x (1) ,. , x (h) T ∈ R h , to a t-dimensional output vector y = y (1) ,.
• Longitudinal Data Collection We consider a publicly available dataset  [13]. We note that once we predict the six thermal parameters, we extract their corresponding PMV value and, hence, it is easy for us to then determine the discomfort situation; • Data Preparation Process Due to the fact that the machine-learning algorithm learns from data, it is critical to feed it with the accurate and meaningful data and to make sure that the data are in a useful format and scale. The learning algorithm is then applied to produce a regressor. -Pre-processing. The input parameter values must be on a similar range and scale. Therefore, we use the common method Min-Max Normalization that normalizes the data values from 0 to 1: v = v -min f max f -min f ( 1 ) where v is the current value of feature f , and max f and min f are, respectively, the maximum and minimum values of feature f ; -Split the Data into training and test set.
Experiments Setup We used scikit-learn library on Python to implement our random forest regressor algorithm. Once we created our indoor parameters prediction model, we then implemented our genetic algorithm to optimize the indoor thermal comfort parameters when observing an indoor thermal discomfort. In addition, to assess the performance of the random forest algorithm in predicting the indoor thermal comfort parameters, we compared it to three other regression-learning algorithms: (i) Multiple Linear regression, which models the relationship between two or more input variables and output variables by fitting a linear equation to input data; (ii) Decision Tree Regression, which develops a regression model in the form of a decision tree structure; (iii) K-Neighbors Regression, which predicts output data based on similarity measures (e.
g. , distance functions) between both input and output data. To be fair in our comparison, we note that, for each algorithm, we tried different configurations and selected only the best configuration, i. e. , that gave us better accuracy. Moreover, to choose the suitable heuristic, we compared the genetic algorithm with the Tabu-search algorithm. The obtained results showed that the genetic algorithm highly outperforms the Tabu-search algorithm and, hence, Tabu-search is not suitable for our study. Therefore, we chose to present only the results related to the genetic algorithm. Table  4  shows the main parameters of both the used dataset and genetic algorithm implementation. We considered the following metrics to evaluate the random forest regressor's performance: • Modeling and predicting time: reflecting the time complexity of the machine learning algorithm to create the prediction model and to make new predictions by using training and test sets, respectively; • R square of the test set: R square is a measure of how close the data are to the fitted regression line of our model (model accuracy).
It is also known as the coefficient of determination; • Adjusted R square of the test set: due to the fact that the R square always increases as we add more predictors (input variables) to the model, Adjusted R square attempts to correct this overestimation and might decrease if the new predictor does not improve the model. Adjusted R square is determined by the following formula: R 2 Adjusted = 1 - 1 -R 2 (m -1) m -h -1 (3) where m and h are, respectively, the number of samples and input variables of the dataset; • Average Relative Error (ARE): represents the sample standard deviation of the differences between predicted and real (observed) values. For each output datum, we predict its value and evaluate the performance against the real value in terms of the ARE as follows, ARE = ∑ m i=1 (y i -y i ) 2 m ; • Average Ground Truth (AGT): for each output datum, AGT explains the average output data value away from the ground truth value when making predictions on the test set.
The AGT is deduced from ARE as follows: AGT = √ ARE. Furthermore, we implement an indoor wireless sensor IoT-based network using Contiki 3 OS and Cooja emulator  [31]  in order to collect indoor data, such as the states of the fans and windows that we need as input data to then predict the indoor thermal comfort parameters. We note that outdoor data such as ambient temperature, humidity, air velocity, etc. may be collected through the Internet network. Cooja is an IoT-based network emulator that runs on the ContikiOS. It supports the simulation of several types of sensor nodes and each node can be programmed individually with its own code. We note that we emulate the hardware of the real sensor nodes Tmote Z1 which is available in the Cooja emulator. In fact, Tmote Z1 sensors comprise ultra-low power MCU and a 2.
4 GHz Transceiver. They include mainly two sensors: ADXL345 accelerometer and TMP102 temperature sensors. In our emulation, these sensors can communicate with each other using IEEE 802. 15. 4 standard, which is a short range wireless communication standard that offers low complexity, low data rate transmission, and low energy consumption. Moreover, we use the Powertrace tool to measure the energy consumption of each sensor node. Table  5  illustrates setting parameters in our emulation and the protocol stack. Our network comprises three types of nodes: (i) terminal sensor nodes which are in charge of sensing targeted data periodically before sending them toward intermediate nodes; (ii) intermediate nodes to send collected data from terminal nodes to the sink node; (iii) a sink node, which is in charge of transferring sensed data to the cloud environment in which we predict the indoor thermal comfort parameters based on both collected indoor and outdoor data.
The collected dataset is from a medium size office (5388. 3763 square meters) of 24 occupants at Philadelphia for a whole year starting July 2012. The data were collected each 15 minutes via both online daily surveys and data logger measurements of the indoor thermal environment, occupants' behavior, and outdoor environmental parameters. The dataset comprises eight features as input data including absolute time, outdoor ambient temperature, outdoor humidity, outdoor air velocity, occupant age, floor number, the state of the fan and the state of window, and six parameters as outputs: indoor air temperature, indoor humidity, indoor air velocity, mean radiant temperature, metabolism rate, and clothing insulation. The size of input space is [m; h]. The number of rows is m = 24 × 24 × 4 × 365 = 840,960 rows and the number of columns corresponds to the number of input features h = 8.
But they are limited in cases of strong overlap (which is usually the case with complex mixtures), and either rely on databases which are specific to a given class of samples  [9] [10] [11]  or require a priori knowledge of resonance patterns. On the other hand, NMR spectroscopists have developed an impressively diverse variety of innovative methods to increase resolution at the data acquisition stage through the design of clever pulse sequences. For instance, 1D pureshift NMR methods, that yield homodecoupled spectra,  [12] [13] [14] [15] [16] [17]  greatly help to simplify 1 H NMR datasets. They also come with limitations, such as a loss of up to 90% of the signal, long duration, possible artefacts, and the removal of information about couplings that could help to correlate signals. Multidimensional NMR methods help to address the complexity of spectra of mixtures by spreading signals over additional dimensions.
In the conventional dimension, processing is done as in non-UF experiments, through zero-filling, apodization of the time-domain data, and Fourier transformation. An additional processing step -performed before FT in the conventional dimension -may be applied to optimize sensitivity and line shapes  [70]. It consists in a spatial apodization of the data to remove signals arising from the edge of the sensitive detection region, where non-uniformity of the gradient profile can lead to asymmetric line shapes. Such processing requires inverse Fourier transforming the k-space data to obtain spatial excitation profiles which are processed by apodization, followed by FT to restore a spectrum in the UF dimension. A processing package for UF NMR has been developed by the authors' group that is based on Python code and is directly compatible with the Bruker Topspin software.
This package is available on the CEISAM website  [71]. In this package, a calibration of the spectral width and chemical shift in both dimensions is needed. The authors' team has also developed another processing package, in Matlab, which is available on request. After the Fourier transform step, a magnitude calculation is usually applied. While spatial encoding methods have been developed that make it possible to use pure-absorption processing in some cases  [72] , they are not widely used because of the specific features, and artefacts, of gradient-based acquisition. 2. 2. 4. Features 2. 2. 4. 1. Spectral width and resolution. Owing to its spatiallyencoded nature, UF 2D spectroscopy is associated with specific features that require compromising between resolution, sensitivity and spectral widths  [46, 49, 50].
In contrast to the case of UF 2D NMR spectroscopy, pure absorption processing is feasible in UF DOSY, although phase errors may remain because of imperfection of the gradient-based acquisition. The processing of the spatially encoded dimension is different from that of UF 2D NMR spectroscopic data. In UF DOSY, the data are Fourier transformed along this dimension as well, optionally after zero-filling and apodisation with, e. g. , a Hamming or Hann function. The second FT step reveals the spatial profile for each peak in the spectrum. The final step is to fit the diffusion decay curves with the modified Stejskal Tanner equation, and build the resulting DOSY matrix. An example of a UF spectrum is shown in Fig. 9. It is interesting to consider the reason for the different processing methods in UF 2D NMR spectroscopy and UF DOSY.
In the former, the information encoded in the indirect dimension is an oscillation frequency, which can be revealed by Fourier transformation of the time-domain data. Fourier transformation is exactly what is achieved by the gradient based-readout. In contrast, in UF DOSY, the information encoded in the indirect dimension is a decay constant, which is revealed (formally) by a Laplace transform of the gradient-domain data. As a result, the k domain data needs to be inverse Fourier transformed to z/G eff data before being fitted with the Stejskal Tanner equation.