dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010; Schlesinger and Jasechko, 2014). While many geographic gaps are caused by the absence of sap flow studies from such areas, some regions where sap flow studies have been conducted are still not represented in SAPFLUXNET. For example, the recent proliferation of Asian sap flow studies  (Peters et al. , 2018)  has not translated into a high representativity of Asian datasets in SAPFLUXNET yet. Similarly, while the coverage of taxonomic and biometric diversity is unprecedented, SAPFLUXNET lacks data for the extremely tall trees  (Ambrose et al. , 2010)  or for other growth forms such as shrubs  (Liu et al. , 2011) , lianas  (Chen et al. , 2015) , and other nonwoody species  (Lu et al. , 2002).
Outlook The public release of SAPFLUXNET has set the stage for the first generation of sap-flow-based data syntheses. The work on these syntheses will fuel new ideas and tools for future improvements of the database, for example new computing approaches for the processing and analysis of sap flow datasets. One example would be the development of robust imputation algorithms to gap-fill time series of sap flow and environmental data, which can take advantage of tools and datasets already developed by the ecosystem flux community  (Moffat et al. , 2007; Vuichard and Papale, 2015). The dissemination of SAPFLUXNET will encourage the use of machine learning algorithms, only occasionally used to analyse sap flow datasets so far (e. g. Whitley et al. , 2013). These approaches can also be used to identify the relative importance of different hydrometeorological drivers of transpiration (W.
L. Zhao et al. , 2019) , or to produce global transpiration maps, by combining SAPFLUXNET with other data  (Jung et al. , 2019). This upscaling of stand transpiration to large areas will also allow addressing broader questions at the regional and continental scale, such as the role of transpiration in moisture recycling  (Staal et al. , 2018). The eventual success of this initiative, in terms of enabling data re-use and contributing to the understanding and modelling of tree water use at local to global scales, will likely encourage the sap flow community to contribute new datasets to future updates of the database. We expect that the development of open-source software for the processing of raw sap flow data  (Peters et al. , 2021; Speckman et al. , 2020) , its eventual widespread use by the sap flow community, and the adoption of standardized calibration practices will increase the quality and intercomparability of future sap flow datasets.
Data availability, access, and feedback In this paper we present SAPFLUXNET version 0. 1. 5 (Poyatos et al. , 2020a) , which contains some small metadata improvements on version 0. 1. 4, the first one to be made publicly available, in March 2020. Both versions supersede version 0. 1. 3, which was initially released to data contributors in March 2019. The entire database can be downloaded from its hosting web page in the Zenodo repository (https://doi. org/10. 5281/zenodo. 3971689, Poyatos et al. 2020a). In this repository, we provide the database as separate. csv files and as. RData objects; see Sect. 2. 4. for details on data structure. Together with the initial publication of SAPFLUXNET in March 2019, we also released the sapfluxnetr R package, available on CRAN, to enable easy access, selection, temporal aggregation, and visualization of SAPFLUXNET data.
Conclusions The SAPFLUXNET database provides the first global perspective of water use by individual plants at multiple timescales, with important applications in multiple fields, ranging from plant ecophysiology to Earth system science. This database has been built from community-contributed datasets and is complemented with a software package to facilitate data access. Both the database and the software have been implemented following open science practices, ensuring public access and reproducibility. Data sharing has been a key component of the success of the FLUXNET network of ecosystem fluxes (Bond-Lamberty, 2018), and many databases in plant and ecosystem ecology now offer open data  (Bond-Lamberty and Thomson, 2010; Falster et al. , 2015; Gallagher et al. , 2020; Kattge et al. , 2020).
SAPFLUXNET fully aligns with this philosophy. We expect that this initial data infrastructure will promote data sharing amongst the sap flow community in the future  (Dai et al. , 2018)  and will allow the continued growth of the SAPFLUXNET database. Here we will show examples of uncertainty estimation for sap flow data in the SAPFLUXNET database. We will address three main sources of uncertainty which affect plantlevel estimates of sap flow: (i) methodological uncertainty, (ii) sapwood area uncertainty, and (iii) radial integration uncertainty. Methodological uncertainty was estimated using the data in the global meta-analysis of sap flow calibrations by  Flo et al. (2019)  as published in  Flo et al. (2021). This estimation can be applied for the main sap flow density methods. We predicted the standard error (SE) of sub-daily sap flow density by fitting, for each method, linear mixed models of reference flow (i.
This uncertainty can be accounted for if the original data employed to obtain the allometry are available. Using these data for one of the datasets in SAPFLUXNET (ESP_VAL_SOR), we first predicted sapwood area, together with the upper and lower bounds of its 68 % predictive interval (equivalent to 1 SE). Then, we estimated the corresponding mean sap flow and its 68 % uncertainty interval (Fig. B3a ). In this case, methodological uncertainty was larger than that caused by sapwood area estimation (Fig. B3b ). Total combined uncertainty (i. e. methodological and sapwood) was obtained by adding their squared values and then taking the square root, following error propagation theory (Fig. B3c ). In this example tree, total uncertainty for instantaneous values is around 400-500 cm 3 h -1 , resulting in a high uncertainty for low flows but low relative uncertainty for higher flows, reaching 13 % at peak flows on 6 June (Fig.
B3c ). When expressed as daily means, this uncertainty will be reduced as temporal averaging decreases the uncertainty by a factor equal to the inverse of the root square of the number of observations within a day  (Richardson et al. , 2012 ). In the same example (Fig. B3c ), a day with high daily mean flow will also show lower relative uncertainty (6 June, 1589 ± 45 cm 3 h -1 , 3 %) compared to one with lower daily mean flow (30 May, 237 ± 45 cm 3 h -1 , 19 %). Table  B1. Fixed-effect coefficients from the linear mixed models fitting reference sap flow density as a function of measured sap flow density using the data from the global sap flow calibration metaanalysis by  Flo et al. (2019). Models for each method included the individual calibration as a random intercept. Sap flow methods are ranked according to their presence in the SAPFLUXNET database, from most to least present: HD (heat dissipation), CHP (compensation heat pulse), HR (heat ratio), HPTM (heat pulse T-max), CHD (cyclic heat dissipation), and HFD (heat field deformation).
Finally, when no information on the variation of sap flow along the sapwood is available, radial integration of point measurements of sap flow density and associated uncertainty can be obtained by applying generic radial profiles according to wood porosity  (Berdanier et al. , 2016)  as implemented in the R package "sapflux" (https://github. com/berdaniera/ sapflux, last access: 8 June 2021). An example application of this procedure shows how different uncertainty bounds can be obtained depending on wood anatomy (Fig. B4 ). In addition, this application shows how assuming a uniform radial profile for ring-porous or diffuse-porous species can lead to substantial underestimation of whole-plant sap flow, compared to a lower impact for tracheid-bearing species.
Number of plants in the SAPFLUXNET database using different radial and azimuthal integration approaches for the different sap flow methods: cyclic (or transient) heat dissipation (CHD), compensation heat pulse (CHP), heat dissipation (HD), heat field deformation (HFD), heat pulse T-max (HPTM), heat ratio (HR), stem heat balance (SHB), and trunk sector heat balance (TSHB).
e. -8. 23) Numeric Longitude, decimal format (WGS84) si_elev Elevation above sea level Numeric Metres si_paper Paper with relevant information on the dataset, as DOI Character DOI link links or DOI codes si_dist_mgmt Recent and historic disturbance and management events Character Fixed values that affected the measurement years si_igbp Vegetation type based on IGBP classification Character Fixed values si_flux_network Logical indicating if site is participating in the Logical Fixed values FLUXNET network si_dendro_network Logical indicating if site is participating in the DEN- Logical Fixed values DROGLOBAL network si_remarks Remarks and commentaries useful to grasp some site- Character None specific peculiarities si_code Sapfluxnet site code, unique for each site Character Fixed value si_mat Site annual mean temperature, as obtained from Numeric Celsius degrees CHELSA si_map Site annual mean precipitation, as obtained from Numeric Millimetres CHELSA si_biome Biome classification based on Whittaker (1970), based Character SAPFLUXNET calculated on MAT and MAP obtained from CHELSA.
It is therefore necessary to consider to what extent these legacies and experiences of the past 10 years have been integrated into the smart city process of Rennes Métropole. What is their impact on the formatting and meaning given to a 'Rennes-style' smart city approach? The SmartRennes action-research project led by a multidisciplinary research team in partnership with Rennes Métropole, aims to shed light on these issues. Bringing to light the history of the 'Rennesstyle' smart city, from data management to a strategic metropolitan project, provides insight into the processes leading to the definition of its objectives and challenges. The deployed digital devices seem to solidify the differences of opinion and tensions surrounding the visions of the smart city. That's why the following questions must be asked: who is the driving force and for what purpose? Which governance for a public control of smart city initiatives? The purpose of this article is then to present the results of a monographic investigation dealing with 'smart city policy' design.
Its political translation by French local authorities is intrinsically linked to the issue of citizen empowerment. The digital technologies acquired and developed in metropolitan areas aim to tackle the challenges of the disintermediation of individuals, and of low participation in urban government  (Walters, 2011). The will to create inclusive city, based for example on the provision of open data, strongly inspires European smart cities. It is therefore appropriate to question the mechanisms, especially legislative, that led to this specificity of French smart city environment: a strong public data management (Box 1).
). 1. The interview grid is divided into three stages: • Over the period from December 2018 to September 2019, we carried out 34 interviews (Figure  2 ) with some of the stakeholders involved in the Rennes smart city. The sample is constructed on the basis of a list of stakeholders explicitly involved in the implementation of the Metropolitan Public Data Service and 3D Experiencity-Virtual Rennes platform. This is why the results must be put into perspective since they only concern the actors involved in this particular projects and not all stakeholders. However, this choice is justified by our research objective. We want to survey the actors officially involved in the governance of the smart city in order to describe the parameters of its conduct. Our methodological design did not aim to assess for example public reception of the Rennes' smart city initiative, but to understand its implementation.
Network Analysis The sociogram exercise (stage one of the interview) has allowed to highlight the engaged players and their relations at a given time, and to question the hypothesis of the development of a new 'urban regime' with the smart city  (Pinson, 2010; Courmont & Le Galès, 2019). The creation of a graph based on the survey of all the players cited during the 34 interviews, as well as the links that bind them, pursues our goal of network analysis. The aggregation of these data and the work carried out based on an algorithm (Force Atlas 2 -on Gephi), allows us to question one of the possible visualisations of current governance of Rennes smart city. This visualisation of the networks does not aim to be exhaustive, but rather to question the coordination of the smart city initiative in Rennes (Figure  6 ).
Evaluating the involvement of the players mentioned during the interviews is carried out in the following manner: the closer the player (symbolised by a round node) is to the centre of the network, the more he aggregates links with others; the wider the diameter of the node, the more the player is cited by his partners. In the interest of anonymity, when agents or elected representatives were mentioned by name, they were placed in their corresponding category. The public player that is Rennes Métropole was precisely identified wherever possible. We chose to regroup the players cited in relation to their key roles of intervention as well as their main policy directions. A detailed analysis of these data will be dealt with below (cf. 3. 3).
Structuring Tools: Controlling, using and Sharing urban Data Implementing Rennes smart city is through the development of several data platforms that takes many forms. Since the arrival of the new municipal team in 2014, lots of data-based partnerships have taken shape. One focuses on 'business uses' of urban data and the Geographic Information System direction initiative for enhancing digitalisation and re-engineering of business process (3D EXPERIENCity®-Virtual Rennes platform). The other concerns the upgrading of open data portal and the redesign and further development of Rennes Métropole data policy (Metropolitan Public data Service and Rennes' urban data interface). This open data portal is supported by the communication direction. As an in-between objective we observe the development of the fab city and the deployment of a local low frequency network and local produced sensors, supported by the digital innovation service.
The political dimension is seen positively given the risks of an economic-oriented smart city. The view of economic players indicates their expectations in terms of public governance of innovation. In fact, although economic players want data and information to be shared, they are aware of the risks of domination of individual interest over the collective interest. It is therefore important to ensure legal regulation by the public player and not by the private player, so that public control promotes the security of citizens and their data. Despite these diverse players' views of Rennes smart city, there is a certain amount of agreement. For example, all stakes are cited or used by the players interviewed and it seems therefore, that the message of Rennes Métropole is understood, even if it is translated differently.
But our analysis allows us today to state that the implementation of such an 'integrated vision' has come up against several issues. The first one lies in defining the interest of the public player in taking action in this field. In fact, players from the digital industry have traditionally put forward the values of efficiency and productivity to justify the implementation of solutions to steer and monitor all the actions of a metropolitan area. These objectives do not seem to stand out in our survey as primary motivations, or in any case they are well below other issues such as those linked to the sustainable city, local democracy or even the sharing of a common political project. In this respect, the prefiguration approach of the Metropolitan public data service which has occupied the players interviewed over two years is an example of a process that voluntary allows time for reflection on the challenges before opting for a technological solution to be developed.
ANNs, in the form of convolutional neural networks (CNNs), are incredibly efficient image classifiers in a supervised learning setting. On the other hand, autoencoder networks can be trained without supervision to learn latent space, and deep reinforcement learning has shown impressive results in beating human experts on several games  [11]. ML is widespread and horizontally suited for many applications, including EDA. While most algorithms can perform identically on curated large datasets  [12] , data can be difficult and expensive to acquire, and small to medium-sized datasets are usual. Selecting the most suitable method for the target application is an important design choice. The available options are many, and, in the next sub-sections, some methods found in EDA are briefly described.
G∈G p(G/x) (5) The classifier uses the Bayes rule to compute the posterior, assuming the features' independence, as indicated in  (6). p(G/x) = p(G) m i =1 p(x i /G) g ∈G p(g )p(x/g ) (6) Since the denominator is a constant for a given feature vector x, the naive Bayes classification decision rule can be formalized only with the prior and the likelihood, as indicated in  (7). Ĝ = arg max G∈G p(G) m i =1 p(x i /G) (7) These classifiers are relatively easy to understand and build. They are easily trained and do not require large datasets to produce effective results. Despite the assumption of independence of the feature, which is not valid for most real-life situations, naive Bayes is a practical approach in many applications. [18]  uses a naive Bayes classifier for fault diagnosis.
The idea behind this study is to estimate the mathematical description of the power consumption as a function of varied input parameters of any analog circuit using neural networks. The proposed approach is generic and even suitable for heterogeneous systems. Moreover, one can perform online power consumption estimations via the proposed strategy. First, analog circuit power measurements are performed via a measurement set-up including a PC for generating different input patterns and saving the power data. Second, the obtained data is used to train the ANN to obtain a continuous mathematical function of the power consumption. The neural networks include three levels: one input, one hidden, and one output layer. The activation functions for the hidden layer and the output layer are sigmoid and linear, respectively.
Exploiting optimization tools for dataset generation is one application of ML-based optimization techniques  [33]. The optimizer manipulates many parameters during its course, creating data points suitable for data mining. Then, they try to select a model and fit it through regression. For such a large population of high-dimensional data points, it is difficult to find a suitable functional for the regressor that can adequately fit the data, while remaining simple enough to allow the solution of the fitting parameters. Some authors have suggested radial basis functions in and posynomialapproximated signomials to this end. In this paper, a committee of regressors is built, each of which fits perfectly in some portions of the design space, and with high error in others, rather than struggling to build a single regressor capable of superior fitting across a very large sample space.
Second, the evaluation takes place, in which SVMs is employed in parallel with a genetic algorithm to reduce the runtime. Finally, the sample boundaries are dynamically adjusted considering the density of feasible samples. The core of the proposed software is developed with C++, where MATLAB convex optimization tool and SPICE simulator are also integrated for searching and performance estimation, respectively. To demonstrate the proposed framework, a folded-cascode operational amplifier and an RF distributed amplifier are optimized and the results are compared with the results of two different circuit synthesis approaches. According to the comparison results, the proposed tool successfully generates solutions for given design specifications within considerably shorter runtimes. Design space exploration is part of a larger framework, which is used as a design assistant tool for analog intellectual property (IP)-(DATA-IP)  [39].
For that purpose, a large database for the current steering DAC is constructed by numerous simulations for different technologies; 1. 5 µm, 0. 5 µm, and 0. 35 µm. Static specification parameters (SSP), Differential Nonlinearity (DNL) error, Integral Nonlinearity (INL) error, monotonicity, and gain error) are defined as the inputs of the ANN while the transistor dimensions are the outputs of the network. General Regression Neural Network (GRNN) is used as the ANN approach. According to the results, the ANN-based design approach can design the circuit for a newer technology. Furthermore, the proposed methodology can achieve better specifications (improved monotonicity and reduction in DNL, INL and gain error. ) Similarly, an ANN assisted technology independent sizing of building blocks (basic current mirrors and differential amplifiers) for analog integrated circuits is studied in  [23].
The models are trained using different technologies; 1. 5 µm, 0. 5 µm, 0. 35 µm, and 0. 25 µm while the test data was obtained for only 0. 18 µm technology to demonstrate the technology independency of the approach. The ANN-based models provide the corresponding circuit design parameters for a new technology without any circuit simulation. GRNN and MLP utilizing the Rprop algorithm are used as ANN. The proposed approach is based on developing a relatively larger database for different technologies, where properly sized circuits are simulated and the results and the corresponding transistor dimensions are recorded. Basic, cascode, Wilson, and regulated Wilson current mirrors are selected for current mirror examples while the conventional differential amplifier is selected as case study circuit.
For both circuit topologies, the ANN provides the width of the transistors for the targeted specifications. To make the approach technology independent, as a straightforward method, the minimum channel length is defined as input parameter as well as the performances (i. e. reference current for mirror circuits, gain, gainbandwidth product, slew-rate etc. ). According to the reported results, GRNN estimates the transistor sizes for current mirror circuits with 94% accuracy while MLP can estimate the sizes for the differential amplifier circuit with 90% accuracy, in which a 10% tolerance was determined for circuit performances. Generation of large training dataset is generally a problem for ANN-based circuit optimization. The proposed approach  [42]  addresses this problem for a current to voltage converter circuit.
The methodology is based on learning the correlation between circuit specifications and circuit elements and determining the particular sizing that satisfies the targeted circuit performances. The flow starts with determination of the prediction target. Once a circuit is determined, the prediction of circuit element values from performances is obtained via a regression analysis. In the second step, the data for learning phase is collected through three sub-steps: data classification, data collection, and normalization. 13 specifications, such as gain, bandwidth, power, etc. , and transistor widths are selected as the outputs and the inputs of the network, respectively. To generate the learning data, an initial input set consisting of 100 different elements is randomly generated and simulated.