dataesr/scientific-paragraphs-categorization huggingface dataset:
This study was further supported by the  EU-JPND Funding for BRIDGET  (FKZ: 01ED1615 ). Stanford: This work was supported by  NIH  grant  R37 MH101495. Sydney: This study was supported by the following  National Health and Medical Research Council  funding sources:  Programme Grant  (no. 566529 ),  Centres of Clinical Research Excellence  Grant (no. 264611 ),  Australia Fellowship  (no. 511921 ), and  Clinical Research Fellowship  (no. 402864 ). The QTIM dataset was supported by the  Australian National Health and Medical Research Council  (Project Grants No. 496682  and  1009064 ) and  US National Institute of Child Health and Human Development  ( RO1HD050735 ). GBF was supported by the funding agencies  FAPESP  and  CNPq, Brazil. CRKC was supported by  NIH  grants  U54 EB020403 ,  RF1 AG041915 ,  RF1AG051710 ,  P41EB015922 ,  R01MH116147 , and  R56AG058854.
JHC was funded by a  UKRI Innovation Fellowship. BC-D was supported by a  NHMRC   CJ Martin Fellowship  ( APP1161356 ). CHYF was supported in part by  MRC  grant,  NIHR BRC  grant. BRG was supported by the  Medical Research Council. LKMH was supported by the  Endeavour Leadership Award  by the  Australian Government Department of Education, Skills and Employment (DESE). TCH was supported by the  National Institute of Health  ( K01MH117442 ). NJ was supported by  NIH  grants  R01 MH117601 ,  R01 AG059874 ,  U54 EB020403 ,  RF1 AG041915 ,  RF1AG051710 ,  P41EB015922 ,  R01MH116147 , and  R56AG058854. AFM was supported by the  Dutch Organization of Scientific Research  under a  Vernieuwingsimpuls 'VIDI' Fellowship  (grant number  016. 156. 415 )SEM was supported by an  Australian National Health and Medical Research Council   Senior Research Fellowship  ( APP1103623 ).
Community-oriented approaches As introduced above, approaches in this category introduce some features to share knowledge within a community of users. These features generally consist of mechanisms to annotate, rank or rate compositions as well as recommendation capabilities. However, there is not a social structure underlying the proposed solutions. One of the most significant solutions in this category is IFTTT  [9]. It is based on trigger-action programming to provide a complete software platform that allows end users to create what they call Applets (condition-action rules), which connect apps, devices and services to trigger one or more automation rules. This platform recommends users with Applets that are created by the community in order to be used and rated, allowing also sharing them by sending their URL through different apps installed in the device (e.
Around 31% of the participants (55 out 177) answered affirmatively. Design. To present the experiment to students, we used the online teaching platform of our university as well as face to face classroom lessons. The experiment was done during a month and it was conducted as follow: 1. We asked students to complete the pre-test questionnaire to collect the data about participants presented above. This was a mandatory task that all of them must to do. 2. We arranged a classroom session to talk to students about the composition of services and present EUCalipTool. We prepared a case study to train students in the use of EUCalipTool. We also sent them a video tutorial of the tool in order to be revised if they needed it. Afterwards, we asked them to use the tool freely at any time they wanted for a month.
In particular, we considered the data presented in  [48]  about the IFTTT platform, which had, in 2018, 14 million registered consumers (although it was not said how many were active) and 75 million of applets since launched in 2010. In average, this means that each user created around 5 applets during 8 years. It is true that the comparison between a tool validated in an academic environment like ours and a platform used by millions of real users can be a little difficult due to the multiple contextual variables that need to be considered. For example, IFTTT users were not asked to use the tool (they had the option of using another) in contrast to our students who were explicitly requested to use only EUCalipTool. However, we think that, on average values, it is a good point of reference to evaluate the participation of students in the experiment.
This same data can be read from another perspective: 53. 5% of those participants who created two or more Composed Services (38 out of 71) had some experience in this activity, and 46. 5% of them (33 out of 71) used a tool for composing behaviour for the first time. Considering all the students that created some Composed Service (102), the platform provided by EUCalipTool was engaging enough to get 33 students who had never created such a composition before to create two or more Composed Services after using the platform. From the 193 Composed Services that students created, 41% of them were created from scratch and the other 59% were created by taking an existing service as a basis. This result reinforce the idea of using a social network as a valuable mechanism to create a repository of predefined elements for end users as it is recommended in  [34].
These results indicate that the proposed algorithms help end users in the composition of services. However, they must be improved to obtain greater acceptance from users. A way these recommendations can be improved is by asking end users to describe the Composed Service they want to create in natural language, before starting to create it. We can match this description with the semantic keywords associated to the service recommended by algorithms in order to highlight those that better fit the Composed Service end users want to create. To do so, natural language processing techniques should be integrated with EUCalipTool. Following with the post-test questionnaire (see the left side of Figure  6 ), around 80% of them stated (by coring this between 5 and 7) that they would use a tool like EUCalipTool if it was available in a market such as Google Play or Apple Store.
If our social network would have supported the testing of services, we would have to consider which artefacts may be published for the testing phase (e. g. services to test and testing reports), the actions that end users may do with them (e. g. execute a service, add a report, validate a report, etc. ), and the integration of the social network with some execution and testing tool. In order to have another representative example, let's consider, for instance, the domain of Web Augmentation  [54]. In this area, end users usually add, alter, or remove features of a web application interface by creating scripts that run on their web browser. A social network focused on the creation of these scripts would help end users to access scripts that may solve their needs, and which would have been created by professional scripters or other end users like them.
This social network may be characterized, for instance, as follows: the artefact to be shared is a Web Augmentation Script, which is published for the implementation and testing phases. The actions that each user can do with a published artefact are two: (1) the installation of the script in order to be tested, and (2) the adaptation of a script in order to create a new one. The social network should be tightly integrated with some web browser and authoring tool in order to allow the deployment and edition of scripts. If additionally, we also want to consider the analysis phase, as approaches like CrowdMock  [55]  do, the artefact to be published can be, for instance, user histories that describe the new requirements to be supported by a Web Augmentation Script. The actions that other users can do with these histories are: (1) to adapt or extend them in order to create new histories, (2) to be associated to some existing script that solves it, or (3) to be supported with the creation and publication of a new script.
The main idea is to propose a conceptual framework that allows us to characterize both:  (1)  the notifications that a user can receive from a social network like the proposed in this work, and (2) the communication resources that must be used to deliver each notification to end users depending on their current situation. Finally, note that, although implemented in the context of EUCalipTool, the definition of the social structure can be applied to other end-user environments for composing services to provide them with social support. In the same way, we have presented a preliminary generalization effort to describe the main concepts that characterizes a social network target at any software development activity and any domain.
data with our CSEM data. On the monitoring aspects, the analysis of changes in electric fields did not allow to identify any CSEM signal related to the thermal stimulation of the RN-15/IDDP-2 well. One possible explanation is the weakness of the time-lapse CSEM signal compared the achieved CSEM survey repeatability as a result of a limited resistivity change over a limited volume within the reservoir. Future reservoir developments in the supercritical reservoir (e. g. hydraulic stimulation, long-term fluid circulation) will most likely generate stronger resistivity variations over a larger volume than during the thermal stimulation of the well. This calibration study provides the basic information for deciding when and how an EM monitor survey must be performed for the monitoring of the Reykjanes geothermal reservoir but also for the definition of the monitoring strategy of similar high-enthalpy geothermal reservoirs.
Our actual time-lapse survey showed that when similar CSEM equipment is used during the baseline and monitor surveys and systematically d-GPS positioned, the remaining key parameter controlling the survey repeatability is the level of external noise. Since the influence of external noise on CSEM data can be artificially reduced (e. g. by increasing the transmitter dipolar moment), it offers the possibility to adapt the survey design to increase the chance of detecting the time-lapse signals of interest. On the contrary, little control is possible on the MT signal to noise ratio and hence repeatability. The time-lapse EM survey acquired over the Reykjanes geothermal reservoir showed indeed that a high CSEM survey repeatability can be achieved with electric field measurements (within a few percent) but that time-lapse MT survey is a challenging task because of the high level of cultural noise in this industrialized environment.
To assess the quality of our CSEM dataset, we inverted the data and confronted the resulting resistivity model with the resistivity logged in the RN-15/IDDP-2 well. We obtained a good match up to 2-3km depth, i. e. enough to image the caprock and the liquid-dominated reservoir but not deep enough to image the reservoir in supercritical conditions. To obtain such an image, we had to jointly invert legacy MT.
, 2018). In addition, industrial activities can sometimes generate low frequency EM noise that can obfuscate the MT signals of interest  (Streich et al. , 2013). To increase the signal to noise and hence reliability of EM monitoring techniques, we propose to complement passive MT monitoring experiments with active CSEM surveys to compensate for the occasional weakness of MT signal in specific frequency bands (e. g. dead band) and/or the presence of strong cultural noise. Indeed, active surveying allows to control and hence potentially increase the signal to noise ratio of acquired data at any point in time (e. g. by increasing the transmitter strength and/or increasing the recording times). In addition, the CSEM source configuration can be such that it provides additional information on the resistivity structure at depth that MT data cannot necessarily resolve.
For instance, a horizontal electric dipole is often used for direct targeting of thin resistive layers, due to its superior sensitivity to resistors compared to MT  (Constable and Weiss, 2006; Weidelt, 2007; Constable et al. , 2009; Commer and Newman, 2009). To test and validate this approach in actual field conditions, we undertook to design and acquire a timelapse MT and CSEM survey over the Reykjanes geothermal field to assess its ability to characterize and monitor the thermal stimulation of the DEEPEGS/IDDP-2 supercritical reservoir, and more generally, any types of natural and/or forced fluid circulations in geothermal systems (e. g. hydraulic, thermal, acid stimulations; geothermal fluid production; produced fluid re-injection). In this paper, after briefly describing the Reykjanes geothermal field, we will first discuss with a synthetic study the benefits of acquiring CSEM data in addition to MT data for resistivity mapping and monitoring.
MT vs CSEM sensitivity The CSEM and MT detectability computed on the simplified 1D resistivity model of the Reykjanes geothermal field (Fig. 4 ) shows that for a similar noise level over the whole frequency band, the sensitivity to a resistivity change within the resistive reservoir is likely to be higher on CSEM data than on MT data, most likely due to the superior sensitivity of the CSEM technique to resistors compared to MT  (Constable and Weiss, 2006; Weidelt, 2007; Constable et al. , 2009; Commer and Newman, 2009). In addition, the use of a CSEM transmitter allows to control and hence potentially decrease the measurement error on apparent resistivities. This provides an unique opportunity to increase the detectability and hence sensitivity of the EM monitoring method to resistivity changes at the reservoir level  (Siripunvaraporn et al.
Time-lapse EM data acquisition Time-lapse CSEM surveys have been acquired in September 2016, while drilling of RN-15/IDDP-2 well and in August 2017, after the thermal stimulation of the well. It used a double orthogonal horizontal electric dipole for the transmitter (Fig. 1 ), 3 km north of the geothermal field providing two polarizations called POL1 ( 900 mlong dipole between E1 and E2) and POL2 ( 900 m-long dipole between E2 and E3). Its position is such that the mid-point of the longest transmitter-receiver offsets (7 km) is located in the vicinity of the target of interest and such that injection electrodes can be installed in conductive superficial material (here, a swamp) to ensure a good electrical coupling of the transmitter with the ground. In the end, we managed to inject repeatedly a current of about 30 A at 560 V with a Metronix TXM22 during both baseline and monitor CSEM surveys.
MT results Seven stations were used for MT acquisition during the baseline survey (see Fig. 1 ). Each MT station dataset consists in 1 h of recordings at 4096 Hz sampling frequency and at least 12 h at 512 Hz. A distant synchronous MT station, located 80 km away, was used as a remote reference (hereafter site 100). MT sounding consistency quality assessment was performed using apparent resistivity and phase curves inter-comparison between single site and combinations of remote reference results. Phase tensor consistency analysis was performed, as advocated by  Booker (2014) : "Smooth variation of the phase tensor with period and position is a strong indicator of data consistency". In order to asses the quality of the MT data in the [1 mHz-128 Hz] band, we show the normalized phase tensor (hereafter PT), i.
CSEM data processing Considering a transmitter injecting a square-wave electric current at a frequency f 0 and a set of synchronous EM receivers recording the horizontal components of the electromagnetic field, CSEM data processing consist in estimating frequency domain transfer functions between the receiver's 4 components of the horizontal electromagnetic field (Ex, Ey, Hx Hy) and the transmitter injected current I along each chosen direction of injection. ⎛ ⎜ ⎜ ⎝ E x E y H x H y ⎞ ⎟ ⎟ ⎠ = ⎛ ⎜ ⎜ ⎝ T Ex T Ey T Hx T Hy ⎞ ⎟ ⎟ ⎠ I. ( 6 ) TF estimation is performed on the harmonics of the injected frequency f 0. The CSEM TF estimation problem (Eq. (  6 )) is simpler than the MT one as i) it reduces to TF estimation between one input I and one output (any component of the EM field) and ii) the input I is stationary (no leverage points are expected).
MT and CSEM static shift corrections Shallow lateral changes of resistivity and topographic features can created charge accumulations at the surface of these inhomogeneities and shift electric field measurements up or down by a factor independent of frequency but phase remains unaffected  (Zonge et al. , 1991). To correct for such effects on CSEM and MT data, we shifted the MT apparent resistivity and CSEM amplitude curves by a factor such that high frequency data (>100 Hz) match the response from a 1D resistivity model built from the 1D inversion of the co-located TDEM sounding. In the end, only the few CSEM/MT stations located close to the geothermal alteration zone outcropping at surface required such corrections.
Interestingly, similar conclusions hold for the entire time-lapse dataset. Indeed, when comparing the repeatability of the amplitudes of the PE major axis of the horizontal electric field with the baseline and monitor signal to noise ratio (Fig. 11 ), the trend is a clear decrease of the repeatability R with increasing signal to noise ratio i. e. with decreasing level of external noise. Since the frequencies of interest for deep reservoir monitoring are low, we have limited our analysis to frequencies less than 10 Hz. This observation demonstrates that for our time-lapse CSEM procedure, the signal to external noise ratio of the repeated EM measurements is the most important parameter to control in order to achieve a good survey repeatability. Contrary to MT monitoring experiments where the practitioner has limited control on the source strength and hence on the achievable survey repeatability, the CSEM signal to noise ratio can be controlled and increased at will by simply increasing the transmitter dipolar moment (e.
Reykjanes spatial and time-lapse CSEM results In this section, we have first performed an inversion CSEM and MT data to confront and validate the CSEM and MT results with the logged resistivities in the RN-15/IDDP-2 well. For this calibration, we only inverted stations along a profile running from the transmitter and crossing the producing geothermal reservoir (Fig. 1 ). For the inversion, we used the 2. 5D MARE2DEM inversion code  (Key, 2016). We subsequently attempted to identify a time-lapse CSEM signal by analyzing CSEM amplitude and phase changes of the electric field between the baseline and monitor surveys.
CSEM inversion We inverted the amplitudes of the PE major axis of the horizontal electric field from seven CSEM stations located in the vicinity of the selected profile  (stations 05, 09, 14, 18, 22 and 24). Inverted frequencies were 1/32 s, 1/8 s, 1/2 s, 2 Hz, 8 Hz, 32 Hz and associated first firth odd harmonics up to 50 Hz. Both POL1 and POL2 transmitter polarizations were inverted. Data from either the baseline or monitor survey were used depending on their signal to noise ratio. We limited the frequency band on the high side to 50 Hz due to the presence of strong external noise (e. g. 50 Hz and harmonics, industrial noise). The starting model of the CSEM inversion was a homogeneous 2 Y m half-space. Numerical simulations of the impact of the land/ocean interface showed that stations nearby the coast may be affected by the presence of the conductive sea over a large frequency band but since the area of interest (RN-15/IDDP-2 well) is located far away from the coast (at least 2 km), we did not include it.
Future 3D inversions will however require to include such an interface and will be the scope of a subsequent paper. To assess the convergence of the inversion and quality of the data fit, we calculated RMS misfits based on measurement errors  (Key, 2016). Measurement errors have been estimated from the external noise levels calculated at the processing stage. The target misfit is set to 1 and we consider the data fit to be satisfactory when misfits are small (as close as possible to unity) and have been significantly reduced during the inversion process (typically several units). Here, initial misfits were in the 10-20 range and dropped into the 2-5 range after 15 iterations, leading a satisfactory data fit over the whole frequency band (Fig. 12 ). Only station 14 has a RMS misfit great than 10, most likely due to a remaining static effect as evidenced by the similar shapes of the modelled and observed amplitude curves.
14 ). Higher values indicate areas where the dataset is highly sensitive to a change in resistivity. The sensitivity of the CSEM setup is clearly non-uniform with the highest sensitivity towards the mid-point between the CSEM transmitter and receiver grid (around 3 km from the transmitter) i. e. in the vicinity of the RN15/IDDP-2 well (located at 3. 7 km distance from the transmitter), confirming that the transmitter and receiver layout is adequate for imaging resistivity variations in this area. It however also shows that the sensitivity at 4/5 km depth is low (at least two orders of magnitudes less than in the first 1. 5 km), possibly explaining why the resistivity values recovered from the CSEM inversion are too low compared to the logged ones. Finally, Fig. 14  also shows that the CSEM sensitivity is poor underneath the transmitter and the most distant receivers (distances greater than 5 km from the transmitter).
Joint CSEM and MT inversion To compensate for the low sensitivity at depth of our CSEM setup, additional constraints (e. g. structural, petrophysical) and/or datasets (e. g. MT, resistivity logs) may be necessary  (Scholl et al. , 2010). In an attempt to increase the resolution of the resistivity image at depths greater than 2/3 km, we have looked into the possibility of jointly inverting CSEM and MT over the area of interest  (Abubakar et al. , 2011). Since our MT dataset is of poor quality, we used the legacy MT dataset collected over the Reykjanes geothermal field instead  (Karlsdóttir and Vilhjálmsson, 2016). We first inverted the apparent resistivities and phases of the non-diagonal components of the MT impedance tensor for seven MT stations nearby our CSEM stations along the profile of interest (Fig.
1 ). Frequencies range from 0. 001 Hz until 100 Hz. Final RMS misfits are close to unity, providing a satisfactory data match (Fig. 15 ). The resulting resistivity model as well as the average resistivities logged in the RN-15/IDDP-2 well are displayed in the middle of Fig. 13. Here also, the shallow conductive smectite-rich caprock is well imaged with inverted resistivities (<5 Ym) in good agreement with the logged values. Nevertheless, the depth of the base of this conductive layer does not match well with the well observations (a few hundreds of meters difference). Contrary to the CSEM inversion, the underlying more resistive chlorite/epidote rich layer is well imaged with highly resistive layers (up to 100 Ym at 5 km depth). To take advantage of both CSEM and MT datasets, we have jointly inverted the amplitudes of the electric field for the CSEM stations 05, 09, 14, 18, 22 and 24 with the apparent resistivities and phases of the non-diagonal components of the MT impedance tensor for MT stations 77, 76, 74, 78, 79, 70 and 139.
CSEM and MT data fit are displayed on Figs. 12 and 15. Overall, misfits are small and similar to the CSEM only and MT only cases, providing a satisfactory  data fit. However, RMS misfits are slightly larger than the standalone cases, simply due to the fact that additional constraints have been introduced in the inversion process by the addition of new data. The resulting resistivity model as well as the average resistivities logged in the RN15/IDDP2 well are displayed at the bottom of Fig. 13. Interestingly, both the shallow conductive smectite-rich caprock and the underlying resistive chlorite/epidote rich layer are now well imaged and in good agreement with the logged values. Furthermore, the depth of transition zone between the caprock and the deeper and more resistive material fits now very well with the well observations.