dataesr/scientific-paragraphs-categorization huggingface dataset:
2 for details). The gwf files have a similar content but with a different structure. They contain 3 channels, one for the strain data, one for the data quality and one for the injections. The channel names differ slightly in O1 and O2 as described in Table  2. Note that the original files produced internally, whose channel names are listed in Table  1 , contain only the strain channel, while the GWOSC files include also the data quality and injection information in the same file.
Data quality and injections in GWOSC files Several types of searches are performed on the LIGO and Virgo data. Those searches are divided into four families named after the type of signals they target: Compact binary coalescences (CBC), GW bursts (BURST), continuous waves (CW) and stochastic backgrounds (STOCH). CBC analyses (see e. g. ,  [149, 150, 151, 152, 14, 8, 153, 110] ) seek signals from merging neutron stars and black holes by filtering the data with waveform templates. BURST analyses (see e. g. ,  [154, 155, 156, 157, 158] ) search for generic GW transients with minimal assumption on the source or signal morphology O1 (4 kHz sampling) O1 (16 kHz sampling) and O2 Strain ifo:LOSC-STRAIN ifo:GWOSC-sKHZ_R1_STRAIN Data quality mask ifo:LOSC-DQMASK ifo:GWOSC-sKHZ_R1_DQMASK Injections mask ifo:LOSC-INJMASK ifo:GWOSC-sKHZ_R1_INJMASK Table  2 : Channel names of the GWOSC frame (gwf) files.
For this reason, the data quality related to transient noises depends on the search type. It is provided inside the GWOSC files for the two GW transient searches CBC and BURST, that are most sensitive to this type of noise. The data quality information most relevant for CW and STOCH searches is in the frequency domain and it is provided as lists of instrumental lines in separate files  [165, 166, 167, 168, 169]. Data quality and signal injection information for a given GPS second is indicated by bitmasks with a 1-Hz sampling rate. The bit meanings are given in Tables  3  and 4  for the data quality and injections, respectively. To describe data quality, different categories are defined. For each category, the corresponding bit in the bitmask shown in Table  3  has value 1 (good data) if in that second of time the requirements of the category are fulfilled, otherwise 0 (bad data).
The meaning of each category is the following: DATA Failing this level indicates that LIGO and Virgo data are not available in GWOSC data because the instruments were not operating in nominal conditions. For O1 and O2, this is equivalent to failing Category 1 criteria, defined below. For these seconds of bad or absent data, NaNs have been inserted. CAT1 (Category 1) Failing a data quality check at this category indicates a critical issue with a key detector component not operating in its nominal configuration. Since these times indicate a major known problem they are identical for each data analysis group. However, while CBC_CAT1 and BURST_CAT1 flag the same data, they exist separately in the dataset. GWOSC data during times that fail CAT1 criteria are replaced by NaN values in the strain time series.
151% (L1) of the run during O2. CAT2 was not used for Virgo in O2. CAT3 (Category 3) Failing a data quality check at this category indicates times when there is statistical coupling between a sensor/auxiliary channel and the strain channel which is not fully understood. This category was not used in O1 and O2 LVC searches, but it is still in the file format for historical reasons. As an example,  [170]  gives the list of all sensors/auxiliary channels used to define the CAT1 and CAT2 flags for BURST and CBC around the event GW150914. Data quality categories are cascading: a time which fails a given category automatically fails all higher categories. Since CAT3 is not used in this specific case and only data passing CAT1 are provided, there is only the possibility that the data pass or not CAT2.
Technical Validation The data repackaged for public use are validated by another independent internal team. In particular, this review team checks that:  • the strain vectors in the GWOSC hdf and gwf files are identical to machine precision to the corresponding strain vectors of the LVC main archives; • the data quality and injection information given to the user correspond to what is included in the original LVC data quality database. The user can get this information in two ways: the bitmask included in the GWOSC files and the Timeline tool described in detail in the section 5. The output of both methods is checked against the database; • the documentation web pages and the content of the present article contain correct and comprehensive information. The data files, the Timeline and the web pages are released to the public once all those checks have been passed.
Usage notes GW detectors are complex instruments, and their data reflect this complexity. For this reason, caution should be taken when searching for GW signals in the detector strain data, taking into account all the details about the usable frequency range, noise artifacts, data quality and injections discussed in this paper and in the references. In particular, the application of all data quality flags described in the previous section does not imply that the remaining data are free of transient noise artifacts. The user can find guidance to analyze the GW data in the tutorials and tools collected in the GWOSC website and discussed in the next sub-sections. The data analysis techniques used to detect GW signals and infer the source properties are described in  [132]  where good practices and advices to avoid common errors are also provided.
Timeline The LIGO and Virgo detectors are not always in observing mode and, even when they are, it is possible that data quality does not meet the requirements of a given analysis. For these reasons it is necessary to restrict analysis to valid segments of data characterized by data quality information that indicates the data is acceptable for the desired analysis. Timeline 18  is a tool to provide a visual representation of available valid data segments over a time interval, together with the related information about data quality and presence of injected signals (see Fig. 5  for an example with the O2 dataset). If the requested interval is short enough, this is shown at the time scale of seconds. For longer intervals, Timeline shows the average value of the selected data-quality bit over nonoverlapping 2 n -second subintervals.
Courses, software packages and tutorials for GW data analysis On-line courses that provide an introduction to GW data analysis ranging from the basics to more advanced topics with hands-on exercises are available from the GWOSC website. 19 Those courses have been recorded during the GW Open Data Workshops. They include lectures on various aspects of GW science and are supported by many tutorials that can be used to understand how to read and analyze the data. The tutorials on the GOWSC website 20  are in the form of Jupyter notebooks  [171]. They explain how to access the data, produce timefrequency spectrograms, carry out matched-filtering searches, infer astrophysical parameters, and manipulate GW localization information. A few tutorials start from first principles and use generic and broadly used analysis software such as scipy  [143] , but most are based on the specialized software packages and libraries that the LVC developed to produce observational results and other scientific products.
A list of those packages is available on the GWOSC website  21  and includes: • the light-weight application readligo to access data; • general purpose application software, such as the LSC Algorithm Library Suite (LALSuite)  [172]  and the Python package gwpy  [173] ; • search-oriented software such as pycbc  [149, 150] , GstLAL [174] and Coherent Waveburst (cWB)  [154] ; • post-processing software for e. g. , parameter estimation such as bilby  [175] , LALInference  [176]  and Bayeswave  [177, 178]. All these packages are open source and freely distributed.
Summary and additional information The LVC is committed to providing strain data from the LIGO and Virgo detectors to the public, according to the schedule outlined in the LIGO Data Management Plan  [16] , via the Gravitational Wave Open Science Center (GWOSC)  [179]. They are also committed to providing a broad range of data analysis products to facilitate reproducing the results presented in their observational papers. Many of these data products are available through the LIGO Document Control Center (DCC); for example, data products associated with the GWTC-1 event catalog  [14]  can be found in  [18]  and  [180]. Many more data offerings are planned for the future. This includes the catalog of observed events and the bulk strain data from the LIGO/Virgo O3 run. More GWOSC Open Data Workshops are also planned.
All users of these data are welcome to sign up with the GWOSC User's Group at https://www. gw-openscience. org/join/. Anyone who uses these data in publications and other public data products are requested to acknowledge GWOSC by following the guidance in  [181]. Publications that acknowledge GWOSC will be listed in https://www. gw-openscience. org/projects/; email gwosc@igwn. org to make sure your publication(s) are included. The Collaborations, and the GWOSC team, welcome comments and suggestions for improving these data releases and products, and their presentation on the GWOSC website  [179] , via email to gwosc@igwn. org. Questions about the use of these data products may also be sent to that email, and will be entered into our help ticket system. More general questions about LIGO, Virgo, and GW science should go to questions@ligo.
Figure3: Sensitivities of the Advanced LIGO detectors during the second observation run (O2), expressed as the equivalent strain noise spectrum of each detector (the blue "Measured" curves). Also shown are the known contributors to the detector noise, which sum to the measured spectrum across much, but not all of the frequency band (i. e. the measured noise spectrum is not fully explained by all known sources of noise). The quantum noise includes both shot noise (dominant at higher frequencies) and radiation pressure noise (dominant at lower frequencies). Thermal noise includes contributions from the suspensions, the substrate and coatings of the test masses. Seismic noise is computed as the ground displacement attenuated through the seismic isolation system and the suspensions chain. The seismic curves differ for H1 and L1 as actual seismic data were used for L1 while the H1 curve is a model that also includes Newtonian noise.
and list the channel names and frame types used to generate the O1 and O2 dataset discussed in this article. In this table and in the following, H1 and L1 indicate the two LIGO detectors (Hanford and Livingston respectively) while V1 refers to Virgo. Downsampling (for the 4 kHz dataset) and replacement with NaNs of bad quality or absent data are the only modifications of the original data.
Acknowledgements This research has made use of data, software and/or web tools obtained from the  Gravitational Wave Open Science Center  (https://www. gw-openscience. org/),a service of  LIGO Laboratory , the  LIGO Scientific Collaboration  and the  Virgo Collaboration. LIGO Laboratory and Advanced LIGO  are funded by the  United States National Science Foundation  ( NSF ) as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. Virgo is funded, through the  European Gravitational Observatory (EGO) , by the  French Centre National de Recherche Scientifique (CNRS) , the  Italian Istituto Nazionale di Fisica Nucleare (INFN)  and the  Dutch Nikhef , with contributions by institutions from  Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain.
and list the channel names and frame types used to generate the O1 and O2 dataset discussed in this article. In this table and in the following, H1 and L1 indicate the two LIGO detectors (Hanford and Livingston respectively) while V1 refers to Virgo. Downsampling (for the 4 kHz dataset) and replacement with NaNs of bad quality or absent data are the only modifications of the original data. Run Det. Channel name Frame type O1 H1 H1:DCS-CALIB_STRAIN_C02 H1_HOFT_C02 O1 L1 L1:DCS-CALIB_STRAIN_C02 L1_HOFT_C02 O2 H1 H1:DCH-CLEAN_STRAIN_C02 H1_CLEANED_HOFT_C02 O2 L1 L1:DCH-CLEAN_STRAIN_C02 L1_CLEANED_HOFT_C02 O2 V1 V1:Hrec_hoft_V1O2Repro2A_16384Hz V1O2Repro2A.
3 °C, light intensity 170 µmol m -2 s -1 ) in April, May and June 2019 (Supplementary Table  S1 ). Living foraminifera were randomly placed on the sediment surface and the displacement of each individual in and on the sediment was recorded using time-lapse photography (i. e. one image every 10 min during 24 h; Nikon V1 with a Nikkor 10-30 mm lens). Then, the images were analysed by using the software Fiji  60. Such a method allowed us to visually follow each individual and extract the coordinates from each of the ~ 144 images combined by the computer program. The coordinates thereby gave the individual's trajectory during the time of the experiment. Additional recovery experiments were performed on one of each experiment carried out at 6 and 36 °C to assess specifically the resilience of H.
Acknowledgements The authors thank the  Région Hauts-de-France , the  Ministère de l'Enseignement Supérieur et de la Recherche  and the European fund for regional economic development for their financial support through the attribution of the CPER research project  CLIMIBIO. Noémie Deldicq PhD  fellowship is funded by the  Ministère de l'Enseignement Supérieur et de la Recherche. Dewi Langlet Post-doctoral fellowship  is supported by the  STARS  research project  COFFEE  of the  Région Hauts-de-France  and the  CPER  research project CLIMIBIO. The authors are grateful to  E. Metzger ,  F. Mermillod-Blondin  and  E. Armynot du Châtelet  members of  N. Deldicq 's PhD thesis survey committee for suggesting to test the effect of temperature on benthic foraminifera. The authors would like to thank the three anonymous reviewers and the editor for their comments that helped to improve the manuscript.
Using the root-mean-squared velocity component u and the energy spectrum E(k), the integral length is defined as L = π dkE(k)/(2ku 2 ) and the large eddy turn-over time as T = L/u. To generate filtered flow fields, the velocity field is filtered at length scale using a Gaussian filter as detailed in  Lalescu & Wilczek (2018). Values for vary from zero (no filtering) to L (the integral scale). Preliminary datasets were generated using all three spherically symmetric filter types described in Lalescu & Wilczek (2018) (i. e. ball filter, Gaussian filter, sharp spectral filter), but the filter type resulted in only small changes in the results with the overall trend being insensitive to the precise filter type. At each filter scale , 10 4 tracer trajectories are integrated in the flow using the corresponding velocity fields, and the velocity gradient tensor is sampled along these trajectories to generate a large database of Lagrangian time histories of velocity gradients along tracer trajectories.
Scale-dependency in particle rotation statistics We use the database of Lagrangian time histories of velocity gradients to compute the orientations and rotations of spheroids by integrating  Jeffery's (1922)  equations (1. 2b) using a fourth-order Runge-Kutta method. After an initial transient of several τ , the statistics of particle rotations reach a stationary state. We calculate the statistical moments of particle rotations after this initial transient period. Figure  2 (b) shows that data for mean-square particle angular velocity follows a power law scaling in the inertial subrange ω 2 p ∼ -4/3. This scaling behaviour, which was first introduced in the context of tumbling rods by  Parsa & Voth (2014)    Bouchet & Verhille 2018;  Pujara et al. 2018; Pujara, Voth & Variano 2019; Oehmke et al.
The east station (SCGE, iGrav032) is positioned in the vicinity of the geothermal extraction wells and the west station (SCGW, iGrav006) is located close to the reinjection wells. In June 2019, we started continuous measurements with iGrav015 at the central station (SCGC), in the transient area between geothermal extraction and injection. Schäfer et al. (2020)  provide further details about the gravity station setup and the continuous observation chronology. Gravity observations are subject to various environmental contributions (e. g. rain, snow and soil humidity). Therefore, we deployed a series of instruments for measuring environmental parameters, which contribute to the total recorded gravity signal. Over more than three years, we acquired a unique data set of high-resolution gravity and environmental time-series.
Earth tides, local pressure effects, polar motion and instrumental drift The largest gravity signal results from the solid Earth and ocean tides and is estimated by tidal modelling  (Agnew 2015). Besides the tidal parameters, we also estimated the barometric admittance factor for each station to be later used for the local pressure residual of the total atmospheric effect (see "Global gravity contributions" below). Both, local tidal models and barometric admittance factors were computed for each gravity station at Þeistareykir using the ETERNA 3. 4 package  (Wenzel 1996). Results of the tidal analyses are given in the Appendix (chap. A1, A2 and A3). Due to the small distance of less than 2 km between the gravity stations, no significant changes of the tidal parameters were observed; with the main tidal waves varying by less than 1% in amplitude factors and less than 1 degree in phase.
Therefore, we applied one uniform model for all three stations comprising the modelling results of SCGW and theoretical long period tidal parameters from solid Earth and ocean tides models (chap. A4, Appendix). The polar motion effect is provided as Earth orientation parameter file by the International Earth Rotation and References Systems Service (IERS; https:// hpiers. obspm. fr/ eoppc/ eop/ eopc04/, Accessed 05 November 2021). We used the TSoft package  (Van Camp and Vauterin 2005)  for computation of the associated gravity contributions. Detailed explanation of these standard corrections is given in  Schäfer et al. (2020). We examined and corrected the individual drift behaviour of the instruments by comparison to absolute gravity measurements  (Hinderer et al. 2015). For this, we performed absolute gravity campaigns (with FG5#206) at each station once every year and adjusted the continuous time-series to the absolute values.
Acknowledgements We gratefully acknowledge  Anette Kaergaard Mortensen  and her colleagues from  Landsvirkjun the National Power Company of Iceland  for the friendly collaboration, supportive provision of borehole monitoring data and for providing the necessary facilities and infrastructure at the Þeistareykir geothermal field. Many thanks to  GWR Instruments, Inc. for their ongoing assistance during operation and maintenance of the iGrav superconducting gravity meters. We also thank  Tilo Schöne ,  Nico Stolarczuk ,  Julia Illigner  and  Cornelia Zech  for installation and maintenance of the remotely operated multiparameter stations at Þeistareykir.
Acknowledgments: We wish to thank the  APSARA National Authority  for permission to conduct remote sensing and collaborative field investigations. We thank  M. So  and  M. Dana  for administrative support. We thank  E. Lustig  and  T. Lustig  for comments and suggestions on aspects of economic geography and  M. E. Smith  and  C. Isendahl  for reading and offering comments on an earlier draft of this paper. Funding: Much of the work on research, planning, and writing of this manuscript was undertaken with the support of the  University of Oregon Global Oregon Faculty Collaboration Fund , supported by the  Global Studies Institute in the UO Office of International Affairs. Parts of this research have been funded by the  Rust Family Foundation , the  Social Sciences and Humanities Research Council of Canada Postdoctoral Fellowship , the  National Science Foundation Doctoral Dissertation Research Improvement Awards  (no.
Wave and Atmospheric Data Gridded wave and sea surface wind data can be obtained from remote sensing equipped with a scatterometer  (Bourassa et al. , 2019)  and from model outputs. However, while satellites collect indirect observations of wind and waves  (Ardhuin et al. , 2018) , data are acquired along tracks, generating maps with an effective resolution of ∼40-50 km and 1 week. Since the wavefield changes at high frequency, that is, for periods spanning a few hours, remote-sensed winds are not the most suitable data set in order to study the wave effect on surface circulation. Concurrently, there exist nowadays consistent and global database about the wavefield, also providing high-resolution wind velocities that are generated by model reanalyses. Such model reanalyses have been extensively validated with different in situ observations  (Berrisford et al.
Geostrophic Velocity Field Geostrophic currents are derived from the sea level anomaly (SLA) provided by the Copernicus Marine Environment Monitoring Service through the product Mediterranean Sea Gridded L4 Sea Surface Heights and derived variables reprocessed (1993-ongoing) (https://resources. marine. copernicus. eu/?option=com_ csw&view=286_details&product_id=SEALEVEL_MED_PHY_L4_REP_OBSERVATIONS_008_051; last access on: February 7, 2019). This product merges the different altimeter missions available (Jason-3, Sentinel-3A, Haiyang-2A, Saral/AltiKa, Cryosat-2, Jason-2, Jason-1, TOPEX/Poseidon, ENVISAT, GFO, and ERS1/2). SLA data are homogenized by the DUACS multimission altimeter data processing system in order to generate gridded L4 absolute geostrophic velocities and optimal reprocessed products for long-term analysis, including the robust estimation of regional mean sea levels trends  (Pujol et al.
It could indicate that this is not a proper trend but rather a part of a longer oscillation or an artifact due to the inconsistency in the SLA data set of 25 years. However, the altimeter product used in this study (see Section 3) is the result of a homogenization procedure among several altimeter satellite observations and is thus considered suitable for trend analysis  (Pujol et al. , 2016). T U and g U present similar trends during the 25 years analyzed (see Figures  11a  and 11b ) with an increment in the eastern Mediterranean Sea and a decrease in the western basin. The global trend is generally positive in regions where the geostrophy is dominant, except in the Libyan Sea where both  cms/s per year (Figure  11b ). The ageostrophic input on the trend of the total velocity module is evaluated through the difference between both tendencies, T U and g U ).
displays an example of the vertical distribution of the Stokes (red arrows), Ekman (blue arrows), and ageostrophic (black arrows) velocity components along the water column in the Mediterranean Sea. It clearly shows that the Stokes component has a smaller influence (only a few meters near sea surface), while the Ekman component has a significant effect at deeper depths. Also, it is important to note how the Stokes component modify the vertical distribution of the ageostrophic velocities in spite of its relative small value as compared to the Ekman velocity (see the difference between blue and black arrows). The velocity fields obtained with this formulation have been validated with the drifters-database provided by the National Institute of Oceanography and Experimental Geophysics Institute (OGS) of Italy.
Data Availability Statement All data are accessible from https://apps. ecmwf. int/datasets/data/interim-full-daily/levtype=sfc/, https:// www. cpc. ncep. noaa. gov/data/teledoc/telecontents. shtml and from https://resources. marine. copernicus. eu/?option=com_csw&view=details&product_id=SEALEVEL_MED_PHY_L4_REP_OBSERVA-TIONS_008_051.