dataesr/scientific-paragraphs-categorization huggingface dataset:
Nomenclature standardization We have observed that various variant databases that form the foundation of annotations for PeCanPIE vary in the structure and quality of variant specification. For example, databases may provide only protein-level annotations, only genomic annotations, or both. Likewise, there are many variations on the Human Genome Variation Society (HGVS)-like protein annotation nomenclature in circulation. The PeCanPIE code attempts to be flexible in parsing, standardizing, and formatting where possible; for example, protein annotations may use either three-character or one-character protein codes (e. g. , "Ser" or "S"), and a number of variations on stop codon formatting have been observed ("Ter," "Term," " * ," "X," and "Stop"). In some cases, partial information such as codon numbers were extracted from an otherwise incomplete annotation.
Acknowledgments This project was supported by the  American Lebanese Syrian Associated Charities (ALSAC) of St. Jude Children's Research Hospital , by a  Cancer Center Support (Core)  grant ( CA21765 ) and a grant to J. Z. ( CA21635 ) from the  National Cancer Institute. The  CReATe Consortium  ( U54NS092091 ) is part of  Rare Diseases Clinical Research Network (RDCRN) , an initiative of the Office of  Rare Diseases Research (ORDR) ,  NCATS. This consortium is funded through collaboration between  NCATS  and the  NINDS. We thank  Dr. Yiyang Wu  for discussions of in silico algorithms. Author contributions: Analysis pipeline design and development (M. N. E. ), web software design and development (A. N. P. , X. Z. , and J. B. B. ), cloud pipeline development (M. N. E. and C. L. M. ), tool development (M.
N. E. , S. V. R. , and M. C. R. ), genomic data analysis (E. R. , D. J. H. , Y. L. , C. A. K. , J. Z. , S. N. , Z. W. , L. L. R. , A. N. P. , T. S. , J. P. T. , M. B. , and M. N. E. ), manuscript text (M. N. E. , J. Z. , E. R. , and C. A. K. ), figure preparation (A. N. P. , M. N. E. , and J. Z. ), database support (M. R. W. ), and project direction and supervision (J. Z. , J. R. D. , and K. E. N. ).
Source URL ClinVar https://www. ncbi. nlm. nih. gov/clinvar/ dbNSFP https://sites. google. com/site/jpopgen/dbNSFP ExAC non-TCGA ftp://ftp. broadinstitute. org/pub/ExAC_release/ release1/subsets/ COSMIC https://cancer. sanger. ac. uk/cosmic/ IARC TP53 http://tp53. iarc. fr/ St. Jude PCGP https://pecan. stjude. cloud/pcgp-explore ALSoD http://alsod. iop. kcl. ac. uk/ NHGRI BIC https://research. nhgri. nih. gov/bic/ LOVD RB1 http://rb1-lovd. d-lohmann. de/ BRCA Share http://www. umd. be/BRCA1/ ASU TERT http://telomerase. asu. edu/diseases. html#tert University of Utah MEN2 http://www. arup. utah. edu/database/MEN2/ MEN2_display. php LOVD APC, MSH2 https://databases. lovd. nl/shared/diseases/05489 Software availability PeCanPIE is available at https://platform. stjude. cloud/tools/ pecan_pie and is one component of St.
Here we present the dataset gathered by the INSIEME seismic network that was installed in the High Agri Valley within the SIR-MIUR research project INSIEME (INduced Seismicity in Italy: Estimation, Monitoring, and sEismic risk mitigation). The seismic network was planned with the aim to study the two induced seismicity clusters and to collect a full range of open-access data to be shared with the whole scientific community. The seismic network is composed of eight stations deployed in an area of 17 km × 11 km around the two clusters of induced microearthquakes, and it is equipped with triaxial weak-motion broadband sensors placed at different depths down to 50 m. It allows us to detect induced microearthquakes, local and regional earthquakes, and teleseismic events from continuous data streams transmitted in real time to the CNR-IMAA Data Centre.
, 2011)  reports seven earthquakes with M w ≥ 4. 5 in the HAV, includ-ing the 1857 M w = 7. 0 Basilicata earthquake  (Mallet, 1862; Burrato and Valensise, 2008) , which was one of the most destructive historical earthquakes in Italy with 11 000 casualties and extensive damage throughout the Basilicata, Campania, Apulia, and Calabria regions. It has also been estimated from GPS velocity and strain rate field data  (D'Agostino, 2014)  that the extensional opening in the axial part of the southern Apennines is about 3 mm yr -1. The INSIEME seismic network was designed and developed in the framework of the research project INSIEME (INduced Seismicity in Italy: Estimation, Monitoring, and sEismic risk mitigation), which was funded in 2015 by the SIR (Scientific Independence of young Researchers) programme of the Italian Ministry of Education, Universities and Research (MIUR) and ended on 23 March 2019.
, 2015). In this paper, we present the INSIEME seismic network and a detailed description of the acquired data which are released through open access. The broadband seismic network has been registered at the International Federation of Digital Seismograph Networks (FDSN, http:// www. fdsn. org, last access: January 2020) with code 3F (https://doi. org/10. 7914/SN/3F_2016, Stabile and the  IN-SIEME Team, 2019). Section 2 details the seismic network from its layout to the acquisition, transmission, and preliminary processing of data. Section 3 is focussed on the description of acquired seismic signals from the data quality of continuous data streams to the waveforms of recorded seismic events. Section 4 provides information on data availability. Finally, our discussions and conclusions are reported in Sect.
The INSIEME seismic network The INSIEME seismic network has been designed to achieve two main purposes: (a) to study the seismic processes related to the occurrence of events belonging to the two clusters of anthropogenic seismicity and (b) to provide the scientific community with new open-access high-quality seismic data for studying such phenomena and for developing methodologies useful to discriminate between natural and anthro-   Serlenga and Stabile, 2019). Anthropogenic seismicity is classified as seismicity induced by continued reservoir (clusters A) and fluid injection (cluster B). The map was drawn using Matplotlib Python library  (Hunter, 2007) , which incorporates the ArcGIS REST Services freely available at http://server. arcgisonline. com/arcgis/rest/services (last access: January 2020).
5-2. 0 m thickness for a couple of weeks. -With the aim to provide an effective added value to the seismic monitoring of the HAV, station locations should not overlap existing stations of operating public and private seismic networks. We performed seismic ambient noise measurements and geological surveys in order to find the most suitable sites according to these constraints, and we verified the access to the site (also for drilling operations of the shallow boreholes), data transmission conditions, and unexpected potential sources of local noise. We evaluated the network performances following the approach proposed by  Stabile et al. (2013)  by considering different configurations of the potential sites that have met as many constraints as possible. The final network configuration is reported in Fig.
The problem was understood and definitively solved on 24 January 2017 at 09:52 UTC. After this configuration correction, the gaps at station INS6 have become comparable to those observed at the other stations of the INSIEME seismic network (Fig. 4 ). In the CNR-IMAA Data Centre there is a Linux server for data acquisition, storage, and processing. The server has been equipped with a hardware RAID controller (redundant array of independent discs), configured as a "RAID 1" disc mirroring, to protect the data in case of drive failure. Our configuration features two 4 TB hard discs (i. e. 8 TB RAW space) in RAID 1 mode, ensuring a N + 1 disc redundancy and a 4 TB total storage capacity. This configuration is an optimal choice for applications requiring high availability. In the future, we will upgrade the system by means of network-attached storage (NAS) in order to store data as well as to enhance the system performance and availability.
Furthermore, on this server the TCP/IP-based SeedLink standard compliant SeisComP3 (https://www. seiscomp3. org, last access: January 2020) software runs for seismological data acquisition. It acquires data in real time from the INSIEME stations and neighbour stations, stores them in a miniSEED file structure, and is able to make those data available through various standard protocols: SeedLink for real-time flow, ArcLink (https: //www. seiscomp3. org/doc/applications/arclink. html, last access: January 2020) and FDSN web services (https://www. fdsn. org/webservices, last access: January 2020) for archived data requests. This SeisComP3 software also holds the stations' metadata and an event database. A schematic view of the data flow from the data logger to the data centre is displayed in Fig.
5. A dedicated web-based system, WebObs  (Beauducel et al. , 2020) , is used to plot a numerical strip chart (called "SefraN") of a representative subset of the configured stations in near real time. SefraN is used to manually identify any event present in the data (Fig. 6 , top panels). It is associated with the Daybook, a database of all the events that have been identified in the data, whether they can be located or not, based on the availability of both P-and Swave pickings. Some regional and global events are prefilled with information gathered from INGV (http://terremoti. ingv. it/webservices_and_software, last access: January 2020) and USGS (https://earthquake. usgs. gov/fdsnws/event/1/, last access: January 2020) FDSN event web services. When a new event is identified, the information is sent to the SeisComP3 database (Fig.
6 ). The event is then manually picked and located (Fig. 6 , bottom panel) with SeisComP3 Origin Locator Viewer (scolv), using the 1-D velocity model from  Improta et al. (2017). The WebObs Daybook displays the event information collected from the SeisComP3 FDSN web service. A preliminary catalogue of HAV seismicity from September 2016 to March 2019 has been produced and is available at https://doi. org/10. 5281/zenodo. 3632419 (Stabile et al. , 2020). 3 Acquired seismic signals.
Data quality in terms of local ground effects According to  Havskov et al. (2012) , local seismic amplifications due to sensor installation on soft ground can greatly affect spectral analyses of low and moderate earthquakes; the use of broadband recordings may become difficult, and the short period signals may be unrepresentative. The absence of meaningful site effects was assessed beforehand for properly choosing the future locations of each seismic station of the INSIEME network. In order to check the validity of our choice and the quality of seismic signals, a further assessment of the negligible site effect on recorded data has been carried out. To this purpose earthquake data have been selected from the preliminary catalogue of HAV seismicity (https://doi. org/10. 5281/zenodo. 3632419; Stabile et al.
For IIE, RIE, and LE data we chose 8, 16, and 32 s wide time windows, respectively, with a corresponding minimum frequency of 0. 125, 0. 0625, and 0. 03125 Hz. In order to have reliable estimates the spectra were evaluated starting from 10 times the respective minimum frequency (i. e. 1. 25, 0. 625, and 0. 3125 Hz). The difference in the selected time windows is related to the dissimilar durations of recorded signals of each category of earthquakes. Before computing the fast Fourier transform, the mean and the trend were removed from the time series and signals belonging to any data category were tapered by applying a Tukey window with 5 % bandwidth. Then, the www. earth-syst-sci-data. net/12/519/2020/ Earth Syst. Sci. Data, 12, 519-538, 2020  computed spectra were smoothed by means of the Konno-Ohmachi function  (Konno and Ohmachi, 1998) , with a bandwidth coefficient equal to 40.
007 Hz. The retrieved time series were processed in an analogous way to the one described before for earthquake data by means of the Geopsy software (Geopsy project; http://www. geopsy. org, last access: January 2020). For each time window and station, the HVNSR was retrieved, taking into account that the horizontal spectrum was computed as the squared average of the two horizontal (EW and NS) components: H = EW 2 + NS 2 2. (3) Finally, the average HVNSR of each station was computed, along with the ±1σ. The retrieved HVSR and HVNSR are represented in Fig. 11. We can assert that most of the stations are characterized by an almost flat H /V curve, independently of the adopted dataset. Furthermore, we separately verified that the choice of an arithmetic mean or a squared average of the two horizontal components is almost completely irrelevant to the consequent HVSR or HVNSR measurements.
Their theoretical arrival times were computed by means of SeisGram2k software  (Lomax, 2008) , which uses the Preliminary Reference Earth Model (PREM) published by  Dziewonski and Anderson (1981). In addition to different seismic phases, in Fig. 14  we are able to observe the dispersive character of surface waves, with lower frequencies, travelling deeper in the Earth and, therefore, faster, arriving at the INS1 station before the higher frequencies.
The events preliminarily located with the Origin Locator Viewer (scolv) tool of the SeisComP3 software are available in the "Preliminary catalogue of High Agri Valley seismicity (southern Italy) recorded by the temporary INSIEME network" CSV file (available at https://doi. org/10. 5281/zenodo. 3632419; Stabile et al. , 2020). The KMZ file "INSIEME-network. kmz" (Keyhole Markup language Zipped, which can be viewed using Google Earth), provided in the Supplement, is an interactive extension of Fig. 1.
Discussion and conclusions In this paper we have presented the data collected by the IN-SIEME seismic network deployed in the HAV to support the research activities of the SIR-MIUR INSIEME project with new high-quality seismic data. Beyond the research purposes of the INSIEME project, we have adopted open-access policy on the continuous data streams acquired by the seismic network since the beginning of data acquisition with the aim to share with the whole scientific community data collected in a very interesting area where both natural and induced events are observed. In this sense, the network can be considered an open-access research infrastructure for studying induced seismicity processes and for developing methodologies for discriminating between natural and induced earthquakes. All eight stations of the network are equipped with broadband sensors (Table  1 ), seven of which are installed in PVC casings at different depths down to 50 m (Table  2 ).
The power supply of stations is provided by solar panels and batteries. Only the INS1 station was initially connected to a power line, but on 14 April 2018 19:16:57 UTC the electric power grid of the Montemurro Cemetery (which powered the station) pre-sented voltage and current instability, thus causing a significant disturbance in the seismic signal. After several attempts the problem was definitively bypassed on 29 June 2018, between 07:19 and 10:04 UTC, by also connecting this station to a power system based on solar panels and batteries. The quality of acquired data has been investigated for each station in terms of both the background noise level and the local ground effects. Our analyses indicate that the installation of sensors in PVC casing at 6 m depth allows a reduction of the noise level up to 20 dB with respect to the noise level recorded at the surface and that there is not a significant difference in the noise level recorded between 6 and 50 m depth.
Furthermore, all the stations are installed at sites with negligible site amplification, except station INS5 where a relevant amplification is observed at about 3. 5 Hz (Fig. 11 ). Between September 2016 and March 2019 we have preliminarily located 852 local natural and induced earthquakes (https://doi. org/10. 5281/zenodo. 3632419; Stabile et al. , 2020) : 43 events (M l ≤ 1. 4) are classified as IIE, 117 events (M l ≤ 1. 8) are classified as RIE, and 692 events (M l ≤ 4. 2) are LE. The availability of a continuous data stream will give the advantage of applying robust automated data analysis procedures for earthquake detection and location such as the master-event waveform stacking method  (Grigoli et al. , 2016) , the multiband array detection and location method  (Poiata et al. , 2016) , single station (e.
usgs. gov/monitoring/gsn, last access: January 2020) or by the GEOFON global seismological broadband network (https://geofon. gfz-potsdam. de, last access: January 2020) for real-time global earthquake monitoring or for global seismology studies. The great coherence of teleseismic recordings provided by such a dense seismic network can also be used as an antenna to track the energy radiated by the propagating rupture along a fault (e. g. Satriano et al. , 2014)  or, more generally, for seismic array applications (e. g. Gibbons et al. , 2008). In addition to the applications mentioned above, all based on earthquake recordings, continuous data streams also provide large datasets of noise data to be processed for the continuous monitoring of crustal temporal variation in seismic wave speed in the study area (e.
g. Clarke et al. , 2011)  or, alternatively, for obtaining broadband surface wave dispersion curves  (Bensen et al. , 2007)  which could be adopted for ambient noise tomography (e. g. Shapiro et al. , 2005)   Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-12-519-2020-supplement. Author contributions. TAS led the writing of the paper and VS prepared Sect. 3. 2 and 3. 3. TAS and CS worked on the seismic network layout and on the choice of the acquisition system. MR installed sensors in PVC casing at different depths and evaluated borehole inclination. TAS evaluated sensor orientation and signal quality in terms of noise level. JMS and ER organized the CNR-IMAA Data Centre, including real-time data transmission from the remote stations to the data centre and the installation of software for data processing.
EG and TAS performed geological surveys and verified the access to the site, data transmission conditions, and unexpected potential sources of local noise. HVSR and HVNSR analyses were performed by VS and MRG. TAS, VS, JB, and SP manually picked the seismic phases and located the natural and anthropogenic seismic events. All co-authors provided comments which contributed to the paper. Competing interests. The authors declare that they have no conflict of interest. SIEME network for their authorizations: Senatro Di Leo (Montemurro), Cesare Marte (Sarconi), Antonio Maria Imperatrice (Grumento Nova), Amedeo Cicala (Viggiano), Franco Curto (Armento), and Mario Solimando (Spinoso). Figures 1, 2, 4, 7, 8, 9, 10,  were drawn using the Matplotlib  (Hunter, 2007)  and/or Ob-sPy  (Beyreuther et al. , 2010)  Python libraries.
• To utilize machine-to-machine (M2M) communication for aggregating ORM messages between the smart meters. Our analysis assumes the following pessimistic scenario: Assuming each household (HH) has a single smart meter, the maximum number of households that can transmit its ORM message during the latency interval is presented in Equation  2. HHs max = RU s max RU s ORM = 10 4 1024 ∼ = 10 (2) RUs ORM is the number of RUs needed for an ORM message in the established pessimistic scenario. To obtain this value, we developed a lite NB-IoT simulator for the uplink layer based on the Matlab Toolkit module: Uplink Waveform Generation. The link-layer model is implemented according to the NB-IoT physical uplink shared channel (NPUSCH) standard (  [19, 20] ). Montecarlo simulations are performed over an AWGN channel to obtain block error rate (BLER) information for the different link-layer configurations.
While it is easy to forecast the consumption of a programmable water heater, it is much harder to estimate the consumption of other devices that may be activated alone or at the same time (Does one always turn on the oven and the dishwasher at the same time?). Some external elements such as the air temperature and the external weather can help to improve the predictions, but they also have a limited impact and correlation, depending on the residential characteristics (solar exposure, gas or electric heating), the thermal inertia of the buildings and the psychological resiliency of the inhabitants. Several datasets for energy consumption can be found in the literature (e. g. , a complete list is presented on http://wiki. nilm. eu/datasets. html). We start analyzing the Individual household electric power consumption dataset  [28]  as this dataset covers more than three years of consumption of a house located at the south of Paris, France.
It presents the overall consumption and the detail of specific sets of devices, with a resolution of one minute between measures. Our first analysis involved the attempt to extract consumption patterns, like for example the profile for each weekday or for different periods of the day (dawn, morning, afternoon, night), which are reputed to present similar behaviors. Neither of the categories we tested led to conclusive results, as illustrated in Figure  6. Indeed, we see that two different days/periods have different profiles even if they belong to the same categories, making it hard to forecast the residential consumption. We also tried to correlate the consumption with the local weather, but the residence from this dataset seems to rely on other energy sources for heating and cooking (gas or oil), so the electrical consumption profile was driven mostly by less powerful devices that have a strong dependence on the user's habits.
Because of this choice, we implemented a simple LSTM network using the Keras library, training it with the same dataset from Hebrail and Berard. After a few experiments, we chose a pipeline composed of one LSTM layer and two dense layers, as depicted in Figure  7. Hence, the network has a visible layer with one or several inputs depending on the length of the historical window we want to evaluate (for example, a 24hs windows with). It also includes a hidden layer with 100 LSTM blocks or neurons, a hidden dense layer with an output size of ten and a final dense layer that makes a single value prediction. The default sigmoid activation function is used for the LSTM block, and the last layer has a non-negative activation constraint (https://keras. io/constraints/) to prevent the algorithms to output negative values, which is unrealistic in the case of power consumption.
Contrarily to the reference dataset, this residence fully relies on electricity for heating and cooking, which can raise different consumption profiles. Three different intervals were compared as input history for the LSTM model: one day, one week and four weeks (roughly a month), as illustrated in Figure  9. The analysis of these forecasts shows that using only 24hs of history brings insufficient input data, and the forecast tends towards the "persistence" of the previous state. Forecasts with a month of history are better but, on the contrary, tend to smooth the consumption and raise the expectation for the lower values. Finally, the forecasts made with a week of history seem to offer a better trade-off between accuracy and the history length. While prediction errors still occur (mostly "false positive" forecasts, like for example on the mark 1-20 on the samples), these estimations are good enough to help a recommendation system or to help to detect potential overloads (summing up the expected consumption from several residences).