dataesr/scientific-paragraphs-categorization huggingface dataset:
We use machine learning algorithms that are trained on a subset of French professors and researchers who have created an ORCID identifier and are thus likely to have carefully selected their own publications. From end to end, this filtering process is controlled for and fine tuned to improve efficiency in terms of precision and recall. Our final data cover 382 cluster proposals, 2,130 distinct research units and 29,886 researchers or professors. Out of this sample, 10,806 are core members of at least one proposal whereas 19,080 are only periphery members. 21,383 scientists are members of at least one funded cluster whereas 8,503 were never funded.
For each researcher, we count the number of papers that are among the top 5% and top 10% most cited papers as well as the share of such papers in their yearly publication outcomes. 12 Scientometric data may also be used to build indicators over the within the cluster, which measures the intensity of the links (Collaborations). 13 e also build variables at the cluster level, to avoid double counting when multiple co-authors from the same cluster are involved in a publication. Specifically we construct the total number of articles in the cluster (PubsRC ), the papers weighted by citations (CitesRC ), the articles weighted by the Journal Impact Factor (JIFRC ), and the average team size (TeamSizeRC ). To appreciate within cluster inequality, we calculate the Gini coefficient of the publications distributions.
In turn, when the variables are normalized by the number of authors, publications in Column (2) and publications weighted by Journal Impact Factor in Column (4), the effect is smaller, in the order of 3%. Are these large or small effects? The financing is rather small when brought back to a figure per researcher: 8. 8 million Euros on average to be shared among 165 researchers in our data, which underestimates the pool of people that could have access to the financing. If the amounts were equally shared among all these 165 researchers, this would represent 50K Euros per individual over the length of the project. In any case this is significantly less than the typical individual grant considered in the literature. The average new NIH grant in 2011 was about 407k USDs 22  and the average ANR grant (per PI) is about 150k Euros.
Arnaud Torres,  Emmanuelle Simon  and  Yves Lecointe  who gave us access to the  Labex data  and helped us collect it. We thank  Pierre Gouedard  for his invaluable help in collecting the data. We thank  Matt Jackson ,  Joan Monras  and participants in seminars and conferences for their comments. This research benefited from the financial support of the  ANR  ( ANR-15-CE26-0005 ) and  Bordeaux IdEx  ( ANR-10 -IDEX-.
Research Infrastructures (RIs) are large-scale facilities encompassing instruments, resources, data and services used by the scientific community to conduct highlevel research in their respective fields. The development and integration of marine environmental RIs as European Research Vessel Operators [ERVO] (2020) is the response of the European Commission (EC) to global marine challenges through research, technological development and innovation. These infrastructures (EMSO ERIC, Euro-Argo ERIC, ICOS-ERIC Marine, LifeWatch ERIC, and EMBRC-ERIC) include specialized vessels, fixed-point monitoring systems, Lagrangian floats, test facilities, genomics observatories, bio-sensing, and Virtual Research Environments (VREs), among others. Marine ecosystems are vital for life on Earth. Global climate change is progressing rapidly, and geo-hazards, such as earthquakes, volcanic eruptions, and tsunamis, cause large losses of human life and have massive worldwide socioeconomic impacts.
Euro-Argo ERIC 2 manages 25% of the Argo international programme of drifting floats, measures ocean properties and transmits the data in near-real-time. It aims to extend 1 www. emso. eu 2 www. euro-argo. eu these measurements to greater depths, and to include other parameters like biogeochemistry. ICOS ERIC Marine 3  measures green-house gas levels which underlie climate change and ocean acidification. LifeWatch ERIC 4  characterizes biodiversity and ecosystem from mountain-to-coastal and deep seafloor, using data analysis and model testing through VREs. EMBRC-ERIC 5  is dedicated to marine biota for advancing in marine biology and ecology research, to mitigate ecosystems degradation to points of no return, and to understanding the multiple stress factors affecting their functioning. The RIs have reached their operational phase (Figure  1B ) and can now begin to increase synergy between these RIs leading to the development of an integrated multi-platform observing system, reducing overlaps, filling gaps, increasing efficiency, adopting new technologies, and increasing spatial and temporal resolution to unprecedented levels.
LifeWatch ERIC: Assessing and Modeling the Multiple Environmental Drivers of Change for Biodiversity and Ecosystem Functioning LifeWatch is the European e-Infrastructure on Biodiversity and Ecosystem Research. LifeWatch's focus includes changes in climate, demographic pressure, pollution, and soil consumption, which are responsible for biological diversity loss and ecosystem functioning change, which ultimately negatively impact the wellbeing of today's society. The overall scientific challenge of LifeWatch is to model biodiversity changes on Earth  (Arvanitidis et al. , 2016). The specific challenges are: (a) reliably and continuously collect, store, manage, process, analyze, and disseminate data on various biodiversity variables, such as diversity, biomass, productivity, coupled with the relevant socio-economic ones; (b) analyze the patterns, processes and consequences from possible changes on biodiversity and society welfare by providing the competitive VREs which offer integrative ways to asses environmental changes and risks; (c) offer prognoses on biodiversity composition, distribution, and functioning under certain scenarios  (Basset and Los, 2012).
Existing Synergies Between the Marine RIs The European marine RIs already have important synergies that can be further developed and enhanced in future to create a more integrated European observing system. For example the EMSO fixed-point observatories can provide highresolution information about a broad range of parameters, representative of different spatial footprints of the ocean depending on parameter  (Henson et al. , 2016)  providing context and validation for the data collected by Argo and ICOS RIs. Examples exist of observations at shared locations such as fixed point stations in the EMSO RI that also support observations for ICOS. The high resolution data from the fixed-point station providing validation for ICOS ships of opportunity data (and vice versa)  (Macovei et al. , 2020).
In this example, the use of shared platforms also improves efficiency in servicing of infrastructure. Time-series data from a range of different marine ecosystems collected at EMSO Regional Facilities can support understanding of environmental change that affects ecological studies at EMBRC labs while EMBRC and EMSO directly benefit from the services of LifeWatch, such as World Register of Marine Species  (Horton et al. , 2019). Another example is the development of biodiversity indicators with physical and biogeochemical data in the frameworks of GOOS, MSFD, and United Nations Division for Sustainable Development Goals [UN-SDGs] (2020). As indicated by the G7 Future of Seas and Oceans Working Group, the most efficient way to proceed for its implementation is to start with the coordinated and integrated augmentation of existing platforms.
TOWARD A COHERENT, INTEGRATED SYSTEM OF RIS There is a need to sustain, integrate, and further develop marine RIs in Europe that generate key data for scientific communities. For example, the European Global Ocean Observing System [GOOS] (2020) contributes to the Global Earth Observation System of Systems [GEOSS] (2020). Some RIs comprises the marine component of the forthcoming (European Ocean Observing System [EOOS], 2020), a coordinating framework designed to align and integrate Europe's ocean observing capacity, together with the Earth Observation (EO) component. Marine RIs are also feeding into the EU's flagship  (COPERNICUS, 2020)   2020 ) Directive should be strengthened. Due to its complexity, the environmental domain should be observed with an integrated, coherent and distributed approach, considering that the processes under study vary from very small to broad observation scales, and from very short to long time scales.
Coordination among environmental RIs started within (ENVRIplus, 2020). This EC project selected common topics across RIs favoring the exchange of experiences and best practices and the study and outlining of common solutions to common problems, including those related to technology, e. g. , power supply devices and low-power; data and metadata; and access to RIs, i. e. , to use the RIs' facilities. The Envri-Fair (2020) project focuses on setting the framework and roadmap for the adoption of the Findable, Accessible, Interoperable, Reusable (FAIR) principles in the data management of each RI. Collaboration is essential to provide solutions to complex issues that cannot be solved by one partner alone. Europe has the resources and capacity to make comprehensive ocean observations for the benefit of society.
Industries can also benefit from the work of RIs through collaboration. Sensor manufacturers have access to a larger market as the network of observatories and platforms expands. They may also benefit from access to test facilities and transnational access programs. Renewable energy, Resource extraction industries (e. g. , oil and gas, deep-sea mining), fisheries and aquaculture can also benefit from access to data products and services provided by RIs and through co-design and co-development, resource assessment, environmental impact mitigation. Novel technology has an important role in making new solutions available to address the current societal challenges. Advances in ocean robotics, for example, and the combination of remote and in situ ocean observations offer new opportunities and reduce operational costs; this will also promote multi-stakeholder contributions by governments, the private sector and citizens  (European Marine Board, 2019).
In MRI, at ultrahigh field, the use of parallel transmit radiofrequency (RF) arrays is very beneficial to better control spin excitation spatially. In that framework, the so-called "universal pulse" technique, proposed recently for head imaging at 7 tesla, gives access to "plug-and-play" nonadiabatic solutions exhibiting good robustness against intersubject variations in the resonant transmit fields. is new type of solution has been defined so far as the result of numerical pulse optimizations performed across a collection of RF field maps acquired on a small population sample (pulse design database). In this work, we investigate an alternative universal pulse design approach in the linear small tip angle regime whereby the database of RF field maps is first transformed into a secondorder statistical model and which then exploits a statistical robust design formalism for the optimization of the RF and magnetic field gradient waveforms.
Experimental validation with an eightfold transmit RF coil for 7 tesla brain imaging shows that this new approach brings some benefit in terms of computational efficiency. Hence, for a design database composed of 35 maps, the computation time initially of 50 min could be reduced down to 3 min. e proposed statistical approach thus enables integration of large databases, presumably necessary to ensure robust solutions. Finally, it provides means to compute flip angle statistics and, along with it, simple performance metrics for quality assurance (RF pulse performance) or guidance in the optimization of TX array architectures.
is obviously can be time-consuming, in particular when the number of TX channels is high  [12]. One alternative approach to handle the variability of the TX field distribution, called universal pulse (UP)  [13] , is to replace the subject-specific TX field distribution with a collection of such TX field distributions acquired on a database of subjects. e rationale here is to achieve, simultaneously across all subjects of the database, good excitation performances, and assume that this property generalizes to the population that the database is supposed to cover (e. g. , the adult population). e validity of this approach has been demonstrated experimentally for various applications and hardware setups  [14] [15] [16]. In those studies, a database of 10 to 20 individuals was deemed sufficient to yield good performances.
However, the amount of data remained too limited to analyze quantitatively how pulse performance changes with the design database, and in particular with its size. Nevertheless, it is reasonable to assume that UP performance increases with the size of the design database; conversely, if a too small database is used to compute UPs, there is a risk of "data overfitting," i. e. , that the solution displays a higher FA shimming performance on the design database than its actual performance on the entire population. To better control the actual performance of UP on the entire population, we propose in this work to model the transmit RF field as a stochastic process, whereby every new subject (giving rise to a new B + 1 map) is a realization of this process. is representation appears very useful as it allows formulating the action of a FA shimming pulse in statistical terms.
We then derive a method to apply the aforementioned statistical robust design method to the design of FA shimming UPs, which uses the second-order statistics of B + 1 (r) instead of the collection of B + 1 maps as input parameter. An experimental validation of the method is provided with data acquired with an eightfold transmit RF coil for 7 tesla head MRI. We demonstrate in particular (1) the utility of statistical robust design approach to boost computational efficiency, (2) the capacity of the method to enable the integration of a large database TX field maps, and (3) the utility of a statistical framework to compute flip angle statistics and to derive simple performance metrics for quality assurance.
In the following, the control field ω 1 (r) is assumed to have Gaussian statistics, i. e. , ω 1 (r) ∼ N(μ(r), C(r)). Let ω (N) 1 ,. , ω (N) 1 be N independent control field distributions (in practice TX field maps measured in N different subjects). If N is sufficiently large, this dataset can be exploited to estimate the mean μ(r) and covariance matrix C(r) of the underlying random process by computing the sample mean and covariance matrix for this ensemble: μ(r) ≃ 1 N 􏽘 N n�1 ω (n) 1 (r) � 􏽢 μv, C(r) ≃ 1 N -1 􏽘 N n�1 ω (n) 1 (r) -􏽢 μ(r) 􏼐 􏼑 ω (n) 1 (r) -􏽢 μ(r) 􏼐 􏼑 *. ( 1 ) For the covariance matrix, we shall use the Ledoit-Wolf  [18]  correction of the sample covariance estimator when the condition N ≫ N 2 c (N 2 c � 64 in this study) is not satisfied. Given a statistical model for B + 1 , we now show that this information is sufficient to compute universal FA shimming pulses and to study the robustness of such pulses with respect to the intersubject variability of B + 1.
( 14 ) Retrospectively, the link with the database-driven objective function (simultaneous design across all subjects of the database of B + 1 maps), denoted here by f UP , can be explained as follows. e latter can be defined as the mean across the design database of the mean squares error of the effective control field: f UP (P) � 􏽘 N s n�1 􏽘 r∈R design P(r)ω (n) 1 (r) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 -1 􏼒 􏼓 2 ⎛ ⎜ ⎝ ⎞ ⎟ ⎠. ( 15 ) Now, if we assimilate sample mean and statistical mean, we obtain f UP (P) � 􏽘 r∈R design P(r)ω 1 (r) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 -1 􏼐 􏼑 2 􏼜 􏼝 � 􏽘 r∈R design ω 1 (P, r) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 -1 􏼐 􏼑 2 􏼜 􏼝, (16) which leads us to the same expression as the one obtained in equation (  13 ) for q � 2. is shows the near equivalence (note however that the objective function defined in reference  [15]  is proportional to the average (L 1 norm) of the FA-NRMSE rather than the L 2 norm and that the brain region for the evaluation of the FA-NRMSE was subjectspecific rather than fixed as in equation (  15 )) of the databasedriven and the statistical approaches when sample mean and statistical mean can be assimilated.
Control Field Second-Order Statistics. e data consisted of a collection of N � 40 B + 1 maps measured on different adult subjects (age � 40 ± 15 years) which were recruited as part of previous studies, described in  [14, 15, 21]. e MRI data were obtained on a parallel transmit-enabled whole-body Siemens 7 T system (Siemens Healthineers, Erlangen, Germany) equipped with an SC72 whole-body gradient insert (200 mT/ m/ms and 100 mT/m maximum gradient slew rate and maximum gradient amplitude, respectively) and the 8Tx-32Rx Nova head coil (Nova Medical, Wilmington, MA, USA) for signal transmission and reception. is RF coil array possesses a CP mode for transmission in which the amplitude and phases at excitation ports 2 to 8 are imposed by the amplitude and phase applied to the first excitation port.
is operation mode thus has only one degree of freedom (the complex amplitude applied to the first excitation port) and is designed to create the TX profile corresponding to a birdcage resonator. e parallel transmission system consisted of eight transmitters (1 kW peak power per channel) driven with the Siemens Tim Tx (Step 2. 3) system. e protocol used to reconstruct the quantitative B + e control field second-order statistics (μ(r), C(r)) of the TX array head coil were computed from the TX field maps of the first (chronology of data acquisition) 35 subjects of the database using the mean and covariance estimators, as described in the eory section. For the estimation of the covariance matrix, the Ledoit-Wolf correction was used due to the limited number of subjects as compared to the dimension of the covariance matrix (64).
1) by solving numerically the problem (same as equation (  13 ) but nonunitary target, T duration of the pulse): min P 􏽘 r∈R design ω 1 (P, r) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 - ϕ target T 􏼠 􏼡 2 􏼪 􏼫 q/2. ( 26 ) In the above expression, R design was defined as the set of positions that belonged to the brain region in at least half of the subjects of the database, i. e. , r ∈ R design ⟺ 1 N 􏽘 N n�1 1 R (n) brain (r) ≥ 0. 5, (27) where R (n) brain denotes the mask of the brain for the n-th subject of the database and 1 R (n) brain (•) is the indicator function of this region. For comparison with SRD-UP, the solution (same parameterization and constraints as for the design of SRD-UP) of the database-driven UP objective was also computed. e database-driven UP objective was defined as follows: min P 􏽘 N s n�1 R (n) brain 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 -1 􏽘 r∈R (n) brain P(r)ω (n) 1 (r) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 - ϕ target T 􏼠 􏼡 2 ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎠.
( 28 ) is pulse is referred to as UP. Finally, a standard hard pulse where the transmit array is used in CP mode was defined. e amplitude of this pulse thus was set to return on average on the design database the target flip angle. We then studied the spatial distribution of the mean and CV of the flip angle for UP, SRD-UP, and CP and compared it with the ultimate FA shimming performance g ∞. For convenience, this performance is attributed to a virtual pulse P which would satisfy (see equations (  4 ) and (  19 )) TP(r) � ϕ target α(r). In the following, this pulse is referred to as UP ∞. Note that α(r) being a local solution, no RF and MFG waveforms can satisfy strictly this relationship unless the k-space trajectory defined by this pulse offers a complete coverage of the transmit k-space trajectory.
is would lead in practice to a prohibitively long FA shimming pulse. Using the five last subjects of the cohort (the 35 first constituting the design database), the flip angle normalized root mean squares error (FA-NRMSE) of UP, SRD-UP, and CP (P one of these pulses, ϕ Bloch (P, r), the FA created by pulse P at location r, 36 ≤ n ≤ 40): 1 R (n) brain 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏽘 r ∈ R (n) brain ϕ Bloch (P, r) ϕ target -1 􏼠 􏼡 2 ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ 1/2 , ( 29 ) was computed on each subject. For the FA-NRMSE computation, the FA maps, ϕ Bloch (P, •), were obtained by numerical integration of Bloch's equations (whereby T1 and T2 relaxations were neglected) rather than using the linear small tip angle approximation (equation (3) For every subject of this test database, a subject-tailored k T -point pulse (minimization of the n th term of the sum in equation (  28 )), possessing the same parameterization as the above universal pulses (SRD-UP and UP), was also designed to minimize the FA-NRMSE.
ese solutions are referred to as ST 36 ,. , ST 40. All pulse optimizations were performed using the second-order active-set algorithm, available in the Optimization Toolbox of MATLAB R2019a ( e Mathworks, Natick, MA), which allows solving constrained optimization problems numerically. e active-set algorithm used the analytical expressions for the Jacobian of the objective function and the RF power, RF energy, and MFG constraints (Hoyos-Idrobo et al. [24] ). e number of iterations was limited to 2 × 10 3. e algorithm however could stop before exceeding this number of iterations provided it reached a local minimum within default tolerance.
e result of the FA-NRMSE analysis made separately on the design and test subjects is presented in Figure  7. e results obtained on the test subjects are compliant with the FA-NRMSE statistics obtained on the design database in the sense that no significant offset (unpaired Wilcoxon ranksum test) was found between the FA-NRMSE obtained in the design and test databases. On the test subjects, the performance was very comparable between UP ∞ , UP, and SRD-UP, with a median FA-NRMSE of 5 to 7%. e subjecttailored pulses returned FA-NRMSEs <2%.
Storage and Computation Time Efficiency. Denoting by N v the average number of voxels in the brain, the storage volume for the first-and second-order statistics of the control field and for a database of N subjects are (N c × (N c + 3) × N v )/2 and N × N v × N c , respectively. us, for N ≥ (N c + 3)/2 (6 in our case), the statistical representation becomes more parsimonious. From the computation time point of view, the processing time (DELL Precision 7540, 32 Gb of RAM, 8 cores, Intel CORE i9 vPro 9th Gen) was 3270 s (669 iteration and 1851 evaluations of the objective function) and 160 s (489 iterations and 1471 evaluations of the objective function) for the computation of UP and SRD-UP, respectively. e computation time for one evaluation of the objective function (and the Jacobian) was 2.
13 s and 0. 26 s for UP and SRD-UP, respectively. As these workloads scaled linearly with the number of voxels (4. 22 × 10 5 in total for the 35 subjects of the database and 1. 1 × 10 4 for R design (see equation (  27 ))), we found that the workloads per voxel were 5 μs for UP and 17. 4 μs for the computation of UP and SRD-UP, respectively. As a result, but keeping in mind that these numbers may depend on the pulse parameterization (e. g. , number of k T -points), the design of SRD-UP is computationally more efficient than the UP design when the size of the design database is greater than 17. 4/5 ≃ 3. 5.