dataesr/scientific-paragraphs-categorization huggingface dataset:
Context and objectives The DoMinEau database (http://www. graie. org/Sipibel/projets. html#DOMINEAU (accessed 07 Dec 2020)) was developed in 2016 in response to a call by Agence Française de la Biodiversité (now Office Français de la Biodiversié, a French public national agency in charge of fostering research and expertise on biodiversity as well as helping to apply public water policies) and for the needs of the SIPIBEL observatory (Site PIlote de BELlecombe -Bellecombe Pilot Site). SIPIBEL investigates pollutants in urban and hospital wastewater and fosters research programmes in four themes (pollutant flows, treatment, risks, and sociology). The objectives of DoMinEau were to structure the datasets to (i) provide information about the pollution of wastewater and receiving water, including micropollutants, microbiology and bio-indicators, (ii) provide an estimation of the data quality and (iii) make the datasets easily accessible, searchable and interoperable by the partners of the project (scientists and water management authorities).
Structure and main features DoMinEau is based on Microsoft Excel. This software was chosen because of its widespread availability, because all the targeted users (scientists and water management authorities) were familiar with its use, and because there was little time awarded in the project for database development. DoMinEau consists of four workbooks, each one organized with several worksheets: • Sites-parameters-and-methods: contains all the metadata associated with the pollutant datasets, incl. sampling points, quantities, analytical methods. The list of quantities includes physical-chemical (temperature, water flow, pH, conductivity), micropollutants, microbiology, hydrobiology indicators and bio-essay indicators. There can be several analytical methods for each quantity. • Campaigns-and-results: contains the sampling dates, sample identifiers, analyses results and a quality assessment for each individual result.
The structure is flexible enough to record any type of sample at any frequency that was collected by the observatory. A sample is defined by a sampling point (location), the start and end dates of sampling and its duration, the type of sample (flow dependent or not) and the conditions during the sampling (river discharge, 48-hrs rainfall, etc. ). The quality assessment method is shown in Figure  5. 6. • Statistics-and-graphics: calculates and displays automatically summary statistics and graphs about the data (see Figure  5. 5). • Data extraction: generates files that can be loaded into statistics and data analysis software, such as R or Matlab®. This is only an extraction utility; it does not carry out additional calculations. A complete workflow was developed for quality assessment. Seven indicators (Figure  5.
6) are used to assign a quality category (correct, dubious, incorrect) to each data. It takes into account not only the quality of the chemical analysis, but also the quality of the sampling itself. For each indicator there is a drop-down list with prepared values (pre-calculated). These values are set manually by the person who imports the data into the database. This step introduces some subjectivity, the reader is referred to the discussion on this important aspect in Chapter 9 on data validation. The most critical step is the coordination with the persons who were actually in the field and collected the samples. At the beginning of the DoMinEau database this was the same person as the data administrator, so that was quite easy. At present, the data administrator works with copies of the field sheets and contacts the data providers if necessary.
DoMinEau is managed by a data administrator at GRAIE (a non-profit organization in charge of the coordination of the SIPIBEL observatory and of data management). In order to disseminate the data to the partners, copies of the Excel database are sent over on request. In addition, the data were all uploaded to the NORMAN network database system (European network for monitoring of emerging environmental substances (https://www. norman-network. com/nds/ (accessed 07 Dec 2020)).
Advantages and drawbacks The main advantage of the DoMinEau database is the effort put on the structure and validation of the data, which results in a more rigorous approach for field work and in particular for compliance with the sampling protocols. It has contributed greatly to improve the quality of the data, which is a good example of a 'virtuous circle'. The statistics and graphic modules provide a first quick overview of the data in real time (as soon as the data is uploaded in the database) that is useful to compare campaigns. The summary dashboard is also useful for project managers and for achieving the project deliverables. The main drawback is caused by the limitations of the selected software, i. e. Microsoft Excel. In terms of data management, new data must be added manually as new lines to the spreadsheet.
With 55,000 records, this is quite cumbersome and it is very easy to make mistakes. The macros (in particular for the automatic statistics and graph generation) can be slow. The system is sensitive to successive updates and versions of Excel. Data management thus requires a lot of time from the Data Administrator (50% FTE [full time equivalent] approximately) and the database being off-line (the only way to transfer data is to send over copies), hence the data are not easily accessible. In practice DoMinEau has not been used as expected by the targeted users (scientists and water management authorities) and did not foster data sharing between project partners as expected. Its use requires significant effort and time. The data are now also stored in the online NORMAN database but they are quite difficult to find and mixed with other datasets.
Case study 3: Data Grand Lyonopen data portal The platform Data Grand Lyon (https://data. grandlyon. com/accueil (accessed 07 Dec 2020)) is far more ambitious than just a water data portal. It has been under development since 2011 as part of the Smart City policy of the Greater Lyon metropolis. Its ambition is to make available all types of data produced over the metropolis territory, that can be used: • To facilitate communication and information exchange between the various management authorities. • To encourage citizens to access the data and encourage potential participation. • By making data freely available to foster innovation and economic initiatives in terms of data analysis and visualization. These data contain items such as aerial photographs, street and cadastral maps, state of traffic, noise data, air temperature data, availability of Velo'v bikes (local bicycle sharing system), localization of disabled persons parking places, bus network alerts, some being available in real-time.
They are produced by different stakeholders over the metropolis territory, public or private, with the ambition of data mutualization and interoperability. Data are distributed according to three types of licence: open data, data delivered for free upon authentication, and data accessible with a licence fee. Most data are open. Data Grand Lyon is based on a metadata catalogue (Figure  5. 7) with a search engine, along with several protocols that: • Take the data from various sources (relational databases, but also plain files). • Format them (middle office, Figure  5. 8). • Make them available using (mostly) standard webservices (front office, Figure  5. 8). Data Grand Lyon does not provide the basic structure of data such as presented in the BDOH case study (see Section 5. 5. 1). However, it can deal with unstructured data to some extent (plain files with stabilized formats and protocols for data updates).
Data Grand Lyon relies mostly on open-source software. In terms of standards for data exchange (see Section 5. 4. 2), Data Grand Lyon implements the WMS/WFS standards for geographic data through MapServer. It also implements the SOS (Sensor Observation Service) for time series using the 52N open-source solution. At the end of 2020, hydrological data were not present in the platform. Only rainfall data (from rain gauges located all over the metropolis territory) are available. They are currently being pushed on to the platform from Excel files. However, hydrological data could seamlessly fit in, using the SOS webservice that can take into account all kinds of data. Data Grand Lyon is an example of the very powerful tools that are now being developed for data storage and access. It is probably the best way to communicate data, even in real-time.
SUMMARY AND TRANSITION This chapter summarized the main wired and wireless technologies that can be used to transfer data from the measurement location in UDSM systems to the location/database where data can be converted into understanding of a system state. From the raw data to databases that allow communication with other ones, this entire process requires hardware, services and IT competences to be conducted properly. Chapters 2 to 4 list the most common sensors for monitoring and here Chapter 5 describes how to transfer, record and store data, i. e. the required hardware to transfer and then record data and make them available for any future use. Once those choices have been made, monitoring network and stations must be designed. Chapter 6 gives details on the macro-(network) and micro-(stations) design of monitoring systems whose processes for data-acquisition, -storage and -access are of key importance.
There are also strong links between the current chapter and subsequent Chapters 7 to 9 in which operation & management, and data uncertainty and data validation are discussed, respectively. In these chapters the importance of employing a high-quality database will become even more clear. As each aspect of monitoring is essential in contributing to obtain information in a controlled and structured manner, a well-wrought design of the data handling allows reconstruction of the 'history' of the data offered to a range a data users. This includes data on maintenance activities, calibration results, validation results and, depending on the application, processed data.
Training set preparation for label-free nuclei detection Six independent acquisitions featuring human and mouse cells treated with compounds inducing various cellular stresses (CM as control, DACT as transcription inhibitor, STS as apoptosis inducer, OL as integrated stress response inducer), were performed using BLUE and TL channels, capturing Hoechst 33342 nuclear staining and transmitted light respectively. In total, 296 × 2 images (publicly available at https://doi. org/10. 6084/m9. figshare. c. 5205278. v2) were acquired and processed as described below (Fig. 1 , Fig. 2 , Fig. 4 ). All processing steps were performed in R using the EBImage  [19] , RBioFormats (https://gith ub. com/aoles/RBioFormats), MetaxpR (https://github. com/asauva t/MetaxpR) and MorphR (https://github. com/kroemerlab/MorphR) packages.
UNET training for label-free nuclei detection A 3-layer UNET model  [16]  was generated using the Google Tensorflow framework  [21]  by means of tensorflow, keras (https://cran. r-pro ject. org/) and unet (https://github. com/r-tensorflow/unet) R packages. The detailed network architecture is shown in Fig. 2. Two other networks, FCN8  [22]  and SEGNET-BASIC  [23] , were implemented and trained for performance benchmarks. The input data were randomly split into "training" and "validation" sets following several ratios, either patch-wise or image-wise, and the different models were trained using an Adam optimizer  [24] , the evaluation metrics being represented by both accuracy and Dice coefficient, defined as: D = 2(P P ∩ P T ) P P ∪ P T ( 2 ) Where P P corresponds to the predicted object pixels, P T to the ground truth object pixels, and D the Dice coefficient.
Naturally, the compiled models are compatible with any tensorflow-capable language (Python, Matlab), and the implementation of the method is not limited to R. Even though the constructed model was able to extend our knowledge on the effects of conventional ICD inducers on RNA synthesis and cell death, it could be further enriched and applied to the detection of other cellular phenomena such as the inhibition of RNA-to-protein translation or protein secretion. Of note, the inhibitors employed here (DACT, MTX, OXA and CDDP) specifically suppress class I transcription, which is responsible for ribosomal RNA (rRNA) synthesis  [34]. Class I transcription mainly occurs in the nucleolus, while other classes of transcription rather take place in the nucleoplasm, perhaps leading to Fig. 5. Transcription and viability kinetics assessment in cancer cells using trained double deep neural network.
Acknowledgments GK is supported by the  Ligue contre le Cancer (équipe labellisée) ;  Agence Nationale de la Recherche  ( ANR ) -Projets blancs; AMMICa  US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Ruban Rose" ;  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085 ),  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  RHU Torino Lumière; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
, 2011; Hellén et al. , 2018). The recently developed Vocus proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF) enables the real-time detection of both VOCs and their oxidation products. With a new chemical ionization source called Vocus, the instrument significantly improves its detection efficiency of product ions compared with conventional PTR instruments  (Krechmer et al. , 2018). Based on a laboratory comparison of different chemical ionization techniques,  Riva et al. (2019)  showed that the Vocus PTR-TOF is sensitive to a large range of oxygenated VOCs. With the deployment of a Vocus PTR-TOF in the French Landes forest,  Li et al. (2020)  observed various terpenes and terpene oxidation products, including a range of organic nitrates. With the benefit of the capabilities of Vocus PTR-TOF to detect hundreds to thousands of molecules, a great challenge arises to analyze the complex dataset where emission sources and atmospheric physical and chemical processes are mixed together.
The application of PMF to highresolution (HR) NO - 3 CIMS data by  Massoli et al. (2018)  identified more HOM factors at an isoprene-dominated forest site in Alabama, USA. Recently, mass spectral binning combined with PMF (binPMF) was proposed as a novel and simple method for analyzing high-resolution mass spectra datasets  (Zhang et al. , 2019). This approach divides the full mass spectra into small bins as input data to PMF, thus avoiding the time-consuming and complicated peak identification. Zhang et al. (2020)  further applied binPMF to subranges of ambient NO - 3 CIMS mass spectra and separated more meaningful factors related to chemical processes yielding HOMs. In this work, we present the first factor analysis on Vocus PTR-TOF datasets to identify and apportion the contribution of different sources and formation pathways to atmospheric organic vapors.
Site description and measurement period The measurement data were obtained during summertime in two forest environments in Europe, the Landes forest in southwestern France and the boreal forest research station SMEAR (Station for Measuring Forest Ecosystem-Atmosphere Relations) II in southern Finland. The field campaign in the Landes forest was conducted from 8 to 20 July 2018 as part of the Characterization of Emissions and Reactivity of Volatile Organic Compounds in the Landes forest (CERVOLAND) campaign. An overview of the Vocus PTR-TOF measurements in the Landes forest has been presented earlier by  Li et al. (2020). The ambient observations at the SMEAR II station were performed during 18 June-18 July 2019. The Landes forest (44 • 29 N, 0 • 57 W) is the largest manmade pine forest in Europe, mainly filled with maritime pine trees (Pinus pinaster Aiton).
More instrumental details have been provided elsewhere  (Krechmer et al. , 2018; Li et al. , 2020). During both campaigns, we operated the Vocus ionization source at a pressure of 1. 5 mbar. Sample air was drawn in through a ∼ 1 m long PTFE tubing  (10 mm o. d. , 8 mm i. d. ). A sample air flow of 4. 5 L min -1 was used to reduce inlet wall losses and sampling delay. Around 100-150 ccm of this flow was sampled into the Vocus, and the remainder was directed to the exhaust. The mass-resolving power of the long TOF mass analyzer was 12 000-13 000 m m -1 during our measurements. Data were recorded with a time resolution of 5 s. During the campaign in the Landes forest, background checks were automatically performed every hour using ultra-high-purity nitrogen (UHP N 2 ). The instrument was calibrated twice a day using a mixture of terpenes (α-/β-pinene + limonene; p-cymene).
, 2011; Yan et al. , 2016). To prepare the data and error matrices for PMF input, the Vocus PTR-TOF data were processed using the software package Tofware (v3. 2. 0; Tofwerk), which runs in the Igor Pro environment (WaveMetrics, OR, USA). The detailed data processing routines have been presented elsewhere  (Stark et al. , 2015). Signals were averaged over 30 min for data processing. Unlike traditional UMR or HR fitting of the mass spectra, in binPMF analysis, the mass spectra were divided into small bins after mass calibration. Due to the greater mass-resolving power of the TOF mass analyzer compared with former binPMF studies  (Zhang et al. , 2019 (Zhang et al. , , 2020)) , a bin width of 0. 01 Th was applied in this study. At a nominal mass N, signals between N -0. 15 and N + 0. 35 Th were included for binning.
colorado. edu/jimenez-group/wiki/index. php/PMF-AMS_ Analysis_Guide#PMF_Evaluation_Tool_Software, last access: 11 March 2021). We ran the PMF up to 10 factors for both subranges. For the low-mass range of 51-200 Th, the signals at m/z 81 Th (C 6 H 8 H + , monoterpene fragment) and 137 Th (C 10 H 16 H + , monoterpenes) were markedly higher than the others. PMF assumes that the data matrix can be explained by a linear combination of different factors. Even if a very tiny fraction of these high peaks is split into a factor, they may dominate the mass profile of the factor. As shown in Fig. S1  in the Supplement, with the inclusion of C 6 H 8 H + and C 10 H 16 H + , the mass profiles of several factors were quite similar and dominated by these peaks. Therefore, the major mass bins of these ions were excluded for further PMF analysis, but their corresponding isotopes were retained, effectively downweighting their contributions to the PMF result.
Dataset overview Figure  2  shows the temporal behaviors of temperature, global radiation, concentrations of O 3 and NO x , and concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR II station. In the Landes forest, the weather was mainly sunny during the observation period (global radiation > 400 W m -2 ), indicating strong photochemical activities. The air mass in the forest was largely influenced by local sources, with wind speeds below canopy lower than 1 m s -1 over the whole campaign. The O 3 concentration fluctuated dramatically between day and night, with the average daytime concentration peaking up to 50 ppb and the average nighttime level falling below 2 ppb  (Li et al. , 2020). The low O 3 concentration at night was probably to some extent caused by its titration by monoterpenes (Fig.
2a ;  Kammer et al. , 2018 Kammer et al. , , 2020)). The Landes forest is known for strong monoterpene emissions  (Simon et al. , 1994). During our measurements, the average mixing ratios of isoprene and monoterpenes were 0. 6 and 6. 0 ppb, respectively. More details about this dataset can be found in  Li et al. (2020). All data in the Landes forest are reported in local time and all data at the SMEAR II station in Finnish winter time (both equal to UTC time + 2). During the measurements at the SMEAR II station, 84 % (26 out of 31) of the days had strong photochemistry (global radiation > 400 W m -2 ), with the rest being cloudy days. The diurnal variations in O 3 concentration were not as dra-matic as those in the Landes forest. In the daytime, the O 3 concentration sometimes reached up to 50 ppb.
Choice of PMF solution and factor interpretation To interpret the PMF results, the most critical decision is to choose the best number of factors. More factors introduce more degrees of freedom to explain variations in the data, but too many factors may cause splitting of real factors and lead to mathematical artifacts without physical meaning  (Ulbrich et al. , 2009). The factor interpretation results in this work are summarized in Table  1. In the factor name, L means the Landes forest and S means the SMEAR II station. For the low-mass range of the Landes forest dataset, the Q/Q exp varied from 15. 5 to 6. 0 for 2 to 10 factors (Q is the total sum of the squares of the scaled residuals for PMF solutions). The larger Q/Q exp indicates underestimation of the errors or high residuals for some bins that cannot be simply modeled by the solution  (Ulbrich et al.
The direct comparison of the mass spectra, time series, and diurnal cycles of six-factor and eight-factor solutions is shown in Figs. S3 and S4. In the six-factor case, the C 4 H 8 H + ion-related factor cannot be separated. With eight-factor results, the factor representing isoprene and its oxidation products is split into two components with similar time series. For the high-mass range of the Landes forest dataset, the Q/Q exp decreased from 2. 5 to 0. 9 for 2 to 10 factors. After evaluation, we choose the eight-factor solution to explain the data. The Q/Q exp value of the eight-factor solution was 1. 1, and the decreasing trend in Q/Q exp obviously slowed down after eight factors. The distribution of scaled residuals as a function of m/z for the eight-factor solution is shown in Fig. S5.
However, in the nine-factor solution, the unknown factor mainly composed of siloxane compounds is split into two factors with similar mass profiles and similar diurnal trends. For the SMEAR II dataset, the optimal solutions of fivefactor and four-factor are chosen for the low-and high-mass ranges, respectively. The Q/Q exp varied from 7. 2 to 2. 5 for 2 to 10 factors in the low-mass range and from 2. 0 to 1. 0 for 2 to 10 factors in the high-mass range. The five factors for the low-mass range are identified as Factor S1 -C 4 H 8 H + ion-related, Factor S2 -monoterpenes, Factor S3 -lightly oxidized compounds with six to nine carbon atoms, Factor S4 -isoprene and its oxidation products, and Factor S5 -monoterpene lightly oxidized compounds. The mass spectra, time series, and diurnal profiles of the four-factor and six-factor solutions for the low-mass range are presented in Figs.
The rotational freedom of the PMF solutions was explored through the use of the FPEAK parameters. For each of the optimal solutions, we varied the FPEAK values between -1 and +1 with a step of 0. 2. For the low-mass ranges of the Landes and SMEAR II dataset, the varying FPEAK values did not change the factor profiles and time series much, indicating that varying FPEAK values from -1 to +1 did not affect the overall results of PMF analysis. For the high-mass range of the Landes measurements, we saw variations, especially in the factor profiles by varying FPEAK values. But after a detailed evaluation, we found no evidence that solutions with FPEAK values away from zero were preferable. However, for the high-mass range of the SMEAR II measurements, the solutions with positive values of FPEAK worked better than the solution with FPEAK = 0 in terms  of factor profiles.