dataesr/scientific-paragraphs-categorization huggingface dataset:
Geocoding 5 Because intensity is determined for each ZIP code, ZIP-CIIM's cover irregular shapes on maps causing the intensity locations to be imprecise. In a macroseismic questionnaire, people are asked to provide their ZIP code to generate to ZIP-CIIM but they are free to provide their street address. (i) To improve the spatial resolution of macroseismic data and (ii) to provide a more realistic spread than in ZIP-CIIM's, the respondent's location (address) is first geocoded to its true geographical coordinates. The USGS has the capability to manually trigger 10 a geocoding in-house algorithm to convert individual addresses into longitude and latitude coordinates by using publically available GPS geocoding services  (Wald et al. , 2011). However, their in-house algorithm to convert addresses automatically is not publically available.
Exploring the address list in the macroseismic ROB-BNS database shows that people frequently abbreviate their street address to their convenience (e. g. av. , av, ave = avenue; str, st = street; °, nr, N° = street number) and regularly make accidental typographical misspells. Although 15 many official geocode systems such as postal services, official cadastre plans, and governmental services provide very accurate locations, these geocoding services usually require an exact and correct address input of the respondent and often cannot deal with abbreviations and typographical mistakes.
approximate quality = address could not be located. Either the ZIP code centre or a community location near the address is returned after geocoding. As the approximate quality is too uncertain to be used, any address that has this quality was rejected from the 30 geocoded databases in this study (Fig. 2 ). Depending on the user's needs, different quality factors and thus different subsets of the database can be selected for macroseismic intensity assessment. For a detailed intensity evaluation of a local site, for instance, only the roof top quality could be used to ensure a correct location. After geocoding, we automatically calculate the epicentral distance from the precise location of the observer to the epicentre obtained by instrumental data, which facilitates the evaluation of intensity distance attenuation.
35 Analysing the address columns in the ROB-BNS (31,929 individual testimonies since 2002) and BCSF (~80,000 individual testimonies since 2000) macroseismic databases shows that ~89% and ~76% of people, respectively, freely give their personal address. The ROB-BNS geocoding system was first applied on 301 inquiries  (235 Solid Earth Discuss. , doi:10. 5194/se-2016 -150, 2016  Manuscript under review for journal Solid Earth Published: 9 November 2016 c Author(s) 2016. CC-BY 3. 0 License. geocoded) submitted by people that reacted to an air blast caused by the accidental explosion of a WOII bomb at 3 January 2014 at Euskirchen (Germany)  (Hinzen, 2014). Although a geocoded map uses fewer responses and presents a subset of the data, the distribution of IDP's significantly improved the interpretation of the macroseismic field compared to ZIP code intensities.
Intensity rescaling 5 In the USGS DYFI system, based on the given answers in the questionnaire, , and  Wald et al. (1999). The questionnaire used by the ROB-BNS network (see  Lecocq et al. , 2009)  is largely based on the standardised questionnaire of  Wald et al. (1999)  using standard statistical procedures and rejections following  Dengler and Dewey (2000). The questions concern typical effects ranging from transient effects to building damage and a decimal community intensity (CDI) value is determined by a computer algorithm. In the ROB-BNS database, 10 first, the obtained intensities were manually checked to delete duplications and to avoid unrealistic answers that lead to unrealistic high intensities for the given earthquake magnitudes. Afterwards, a floor-level correction was applied for people living at higher floors in a multi-story building to avoid intensity overestimation due to wobbling of upper floors (prescribed in the EMS-98 guideline, p.
Intensity grid cell analysis In a geocoded dataset, IDP's that are statistically too high or too low (because the respondent did not answer all 20 questions correctly) are not ruled out and may lead to slightly over-or underestimated intensities. Because of these artefacts, the distance decay of a geocoded dataset has a larger scatter than the distance decay of averaged intensities from a ZIP code dataset  (Haase and Park, 2006). To account for this statistical problem,  Wald et al. (1999)  subdivided ZIP codes by regularly-sized grids of a few kilometres, i. e. smaller than an average community polygon, for which more realistic average intensities can be calculated. In this work, a similar grid cell approach 25 is followed: after geocoding all IDP's, we first structure the model area where the earthquake was perceived in 100 km 2 (10 km by 10 km) grid cells.
Integrating macroseismic databases The 2011 Goch event was widely felt in Germany, Belgium, Luxemburg and The Netherlands. Macroseismic data was collected by the ROB-BNS network, by the Royal Netherlands Meteorological Institute (KNMI) in The Netherlands, and by the Geological Survey of Nord-Rhein Westfalen (NRW-GD) in Germany, a network that overlaps with the ROB-BNS network. Felt reports for the 2015 Ramsgate event were collected at following 15 seismological institutes: the ROB-BNS network for the Belgian territory, the BCSF for the French territory and the BGS in the UK. Apart from these national inquiries, also the international EMSC and USGS agencies assembled DYFI witness reports of the two earthquakes (Tables  1  and 2 ). To fully understand the effect of seismic radiation across the national borders of countries where the Goch and Ramsgate earthquakes were perceived, it was crucial to collect and merge all available macroseismic 20 information from these international agencies.
ROB-BNS 15 The ROB-BNS database (3294 responses) shows the largest geographical spread, mainly because the questionnaire is provided in four languages (see Appendices A & B) allowing to survey far across the national borders. A total of 2307 addresses (69%) of the ROB-BNS database could be geocoded. This rather low percentage is mainly because a large amount of German responses lacked street address information. After floor-level correction 2134 responses (65%) remained for intensity assessment (Table  1 ). Floor-level correction affects the 20 grid cell intensities of large cities: Brussels and Verviers in Belgium and Cologne, Düsseldorf, Wuppertal and Essen in Germany as they all rescaled from intensity III to intensity II. Intensity IV results for all cells in an area of 30 km around the epicentre.
NRW-GD To 1149 entries of the NRW-GD questionnaire, a proper intensity could be assigned. As the questionnaire is only provided in German language, almost all responses, expect one from Heijen (NL), were submitted from in Germany. NRW-GD did not share addresses but only provided truncated, three digits individual coordinates. Due to the large 100 km 2 cell size, locations are still properly grouped within the correct cell. Intensity data could not 5 be floor-level corrected as this question is missing in the questionnaire. Remarkably, the NRW-GD database lacks a single not-felt report, intensity I is completely lacking in their distribution (Fig. 5 ). The half intensity units, the missing floor-level question and the lack of intensity I reports strongly affects the intensity distribution in the grid cells (Fig.
5 ) with respect to the other institutes. In the German affected area, NRW-GD grid cell intensities are systematically higher (on average a half unit but sometimes more than one unit) than the grid cell intensity of 10 ROB-BNS. Grid cell intensity in Düsseldorf, for example, was rescaled after floor-level correction to CDI 2. 5 in the ROB-BNS database whereas in the NRW-GD the intensity is CDI 3. 3. There are also many isolated grid cells, sometimes 100 km from the epicentre (e. g. Wuppertal, Dortmund) that still show intensity IV although in the ROB-BNS database intensity III is assigned to these cells, or even are floor-level corrected to intensity II.
EMSC 15 At the EMSC, macroseismic data are collected in two different ways: (1) through a classic questionnaire, (2) via a list of thumbnails (cartoons) describing shaking level  (Bossu et al. , 2008). The information gathered from the questionnaires is divided in an optimal number of clusters and a representative intensity is assigned to each cluster  (Amorèse et al. , 2015). At least five responses are needed in order to assign an intensity to a cluster. The cluster data is comparable to a grid cell, although clusters may have different sizes. The floor-level question is on the 20 EMSC web form but is not taking into account in the intensity estimation. A total of 259 people reported to the EMSC via the thumbnails and 269 via the questionnaires. Plotting individual thumbnail locations on a satellite map shows that the locations are often not geocoded for rooftop quality.
USGS After collecting individual responses, the USGS assigns a weighted value to the answers of different questions 30 (i. e. the felt index, shelf index, damage index, etc. ). The responses are then averaged to reflect a consensus of the grouped answers. We used the weighted, one digit, CDI intensities of 1038 people that reported to the USGS to have felt the Goch earthquake. Personal details were protected by truncating location coordinates to two digits. The USGS grid cell intensities are similar to the ROB-BNS intensities, despite the low amount of grid cells on the USGS grid cell map (Fig. 4 ). A floor-level correction could not be applied due the missing question. Yet, most 35 people reported from The Netherlands to the USGS. Rescaling the KNMI data for floor level hardly affected grid cell intensity due the low amount of reports from multi-store buildings.
Comparing and merging databases With respect to the epicentre, the 2011 Goch earthquake was felt from Amsterdam (NL) in the NW, Paderborn (DE) in the East, Darmstadt (DE) in the SE to Brussels (BE) in the SW. For these four countries involved, 10,281 5 people responded to the online questionnaires of six institutes (Table  1 ). The furthest submitted response came from Lille (FR) at 245 km. After geocoding or by using the truncated location provided by the institutes, 8218 entries could be used for intensity analysis. Before merging macroseismic data of all institutes, first the quality of the different macroseismic sources are evaluated. The distribution histograms (Fig. 5 ) show the individual and grid cell intensity distributions of all entries and for each institute separately, binned by half intensity units.
The 10 number of cells in each histogram corresponds to the same amount of cells in Figures  4  and 6. Cells with less than three responses are not included in the grid cell distribution. Several observations can be made when comparing the distribution of the individual entries with the grid cell histogram:  Although high intensity individual IDP's exist, merging IDP's in grid cells and averaging their intensity reduces the influence of overestimated intensities; 15  Grid cell intensity assessment results in a normal distribution of the macroseismic population whereas such a distribution is lacking in the individual database, no matter which institutional data is considered. This is a plausible result given the fact that the area to which intensities II and III are assigned is larger than the epicentral area to which intensity IV is assigned.
It also shows that intensity I has been systematically underreported. 20  Merging intensities from national agencies results in a denser coverage of the felt area (Fig. 4 ) and higher number of responses (Fig. 5 ) than the international USGS or EMSC inquiries alone.  The mean intensity of the grid cell distribution of transfrontier inquiries (ROB-BNS, EMSC and USGS) is lower (I = 2. 5) than the mean intensity of national inquiries (I = 3 for KNMI and I = 3. 5 for NRW-GD). It seems plausible that the grid cell distribution of NRW-GD database is higher than KNMI because more 25 responses are gathered from the epicentral area in Germany, whereas KNMI has no entries from this area.  The NRW-GD received many responses, some of which were located more than 150 km from the epicentre. Yet, intensity I (non-felt) responses lack in their database.
Because intensity data could not be floor-level corrected, intensities of cities are mostly higher than in the database of other institutes. Because of this, the NRW-GD intensity distribution is shifted towards higher intensities compared to other institutional 30 distributions (Fig. 4 ). Based on these comparisons it is concluded that the NRW-DG intensities are systematically overestimated. To avoid intensity overestimation, NRW-GD data were further not used when databases are merged for intensity mapping and attenuation but are used to discuss the felt impact of the Goch earthquake. After geocoding, floor-level correction and exclusion of NRW-GD data, 7068 entries remained for final intensity 35 assessment. 663 people reported to have not felt the earthquake (intensity I). An intensity could be assigned to 309 grid cells (Fig.
the furthest grid cells to which a reliable intensity could be assigned, a small intensity increase is given by the IAR of the grid cell data (Fig. 7B ). This increase exemplifies an intensity amplification that is caused by the grid cells located between Liège (azimuth of 197°) and Brussels (azimuth of 237°). Remarkably, people from the cities of Assen (150 km, pop. 69k) in The 20  Netherlands and Osnabrück (145 km, pop. 196k) , Bielefeld (168 km, pop. 327k), Siegen (153 km, pop. 108k) and Koblenz (175 km, pop. 110k) in Germany did not report to have felt the earthquake, although they are at closer or similar distance than those living at Brussels. The lack of responses from these large cities shows that the amplifying shaking effect in the IAR between 140 km and 170 km is caused by the Belgian macroseismic data and is clearly not a matter of population density.
Merging databases FIGURE 10 20 With respect to the epicentre, the 2015 Ramsgate earthquake was felt from Liège and Verviers (BE) in the ESE, Lille and Calais (FR) in the SE, Norwich (UK) in the North, Stoke-On-Trent in the NE and Brighton in the SW. For the four countries involved, 6,222 people responded to the online questionnaires of five different institutes to have felt the Ramsgate earthquake (Table  2 ). Similar as to the Goch earthquake, the complex macroseismic situation in Europe is reflected in the fragmented datasets of the Ramsgate earthquake, but the subtotal of entries 25 to national institutes (Fig. 9 ) fully covers the felt area. To illustrate the shaking effect homogeneously, a 100 km 2 grid cell intensity map was created after merging the clustered data of the EMSC, the 25 km 2 grid cells of the BGS and after calculating grid cell intensities for the USGS, BCSF and ROB-BNS IDP's (Fig.
Conclusions Construction of transfrontier macroseismic maps in Europe remains challenging due to a variety of questionnaires 10 and differences in intensity calculation (up to one intensity unity) between different seismological agencies. These differences highlight the importance of the European macroseismic scale for merging data. It is necessary to collect the highest possible amount of questionnaires to ameliorate the liability of the macroseismic intensity analysis and national agencies are in the best place for that as they still gather the highest number of responses (see subtotals in Figs. 5  and 9 ). However, harmonisation between institutional questionnaires is necessary in order 15 to use the full amount of transfrontier collected testimonies. We demonstrated the functionality of geocoding and applying the grid cell method for merging (inter)national macroseismic databases.
Although less data points are used, geocoding intensity locations minimizes the location uncertainties in a macroseismic map. A grid cell intensity map shows a more conclusive intensity distribution than a ZIP code intensity map. Grid cell maps are self-explanatory and avoid errors on the 20 often subjective contouring for isoseismals. Gridding moreover allows merging multiple DYFI macroseismic datasets collected by different seismological institutes in which the individual locations are (made) imprecise. Sharing grid cell information between seismological institutes can thus be a solution in macroseismic data exchange of transfrontier-felt earthquakes because all statistical intensity information is stored in the grid cells and legal issues, such sharing a person's privacy, are avoided. 25 Post-processing internet macroseismic data strongly improves the quality of real-time intensity evaluation of individual agencies.
For a reliable post-seismic transfrontier intensity assessment, we advise to start with the inquiry of the institute that has the best spatial cover. Geocode if possible (if using Google remind the 2500 address limit/day) and make sure a floor-level correction can be applied; if not, add the question to your enquiry to avoid intensity overestimation. Create a network of volunteers that can be alerted in real-time to obtain 30 more intensity data and increase the number of not-felt, intensity I entries. Collect and merge other institutional data to obtain the best coverage of the felt effect. Macroseismic grid cell intensity analysis of the ML 4. 3 2011 Goch and the ML 4. 2 2015 Ramsgate earthquakes proved that the non-concentric macroseismic distributions of both events are controlled by distance attenuation, the Q-factor in the bedrock and in the overlying sedimentary column, and by bedrock depth.
3 (Ms 4. 1, MW 3. 7) Goch earthquake (DE) and the 22 May 2015 ML 4. 2 (MW 3. 7) Ramsgate earthquake (UK). Both earthquakes caused non-circular distributions of intensities across NW Europe and their perception in Belgium, Germany, The Netherlands, Luxemburg, France and the UK definitely classifies them as 'transfrontier'. For each earthquake, all institutional DYFI responses were collected and merged. This study provides a post-seismic (not Solid Earth Discuss. , doi:10. 5194/se-2016-150, 2016 Manuscript under review for journal Solid Earth Published: 9 November 2016 c Author(s) 2016. CC-BY 3. 0 License. yet applied in real-time) methodology how European macroseismic datasets can be successfully merged and demonstrates why the studied events had non-circular intensity distributions taking into account source mechanism, population density, local geology, bedrock depth and intensity distributions of past earthquakes.
Earth Discuss. , doi:10. 5194/se-2016-150, 2016 Manuscript under review for journal Solid Earth Published: 9 November 2016 c Author(s) 2016. CC-BY 3. 0 License. of the collected data. Hence, merging the networks of the subscribed informants allows having more data, removes the spatial variability and ensures a better reliability of the macroseismic information. Examples of other transfrontier macroseismic maps based on online testimonies, e. g. 2003 Rambervillers (FR), 2004 Waldkirch (DE), all show a good equivalence in intensities at the borders which justifies merging databases. After database request, ROB-BNS, KNMI, BCSF and USGS intensities were provided in CDI with one 5 digit. The NRW-GD uses a different approach for intensity calculation and only uses half intensity units. EMSC and BGS macroseismic data were provided in integers.
Acknowledgements The authors sincerely thank the 16,503 individuals that responded to both earthquakes via different online platforms. We further acknowledge the following people for providing the institutional macroseismic data: B. 15 Dost and  G. -J. van den Hazel  (KNMI),  K. Lehmann  ( North Rhine-Westphalia Geological Survey ),  R. Bossu  and  G. Mazet-Roux  (EMSC),  D. Wald  and  V. Quitoriano  ( USGS ), and  B. Baptie  and S. Sargeant (BGS). R. Gold and the Seismology. beteam are thanked for discussions on the content. Figures were created in QGIS (2016). K. Van Noten is supported by the  Fonds de la Recherche Scientifique (FNRS -Belgium)  under grant  PDR T. 0116. 14.
Solid Earth Discuss. , doi:10. 5194/se-2016-150, 2016 Manuscript under review for journal Solid Earth Published: 9 November 2016 c Author(s) 2016. CC-BY 3. 0 License. of the collected data. Hence, merging the networks of the subscribed informants allows having more data, removes the spatial variability and ensures a better reliability of the macroseismic information. Examples of other transfrontier macroseismic maps based on online testimonies, e. g. 2003 Rambervillers (FR), 2004 Waldkirch (DE), all show a good equivalence in intensities at the borders which justifies merging databases. After database request, ROB-BNS, KNMI, BCSF and USGS intensities were provided in CDI with one 5 digit. The NRW-GD uses a different approach for intensity calculation and only uses half intensity units. EMSC and BGS macroseismic data were provided in integers.
MeCP2, the founding member of the MBD protein family, has been described to function as a transcriptional silencer through association with corepressor complexes mediated by its transcriptional repression domain (TRD)  (3) (4) (5). Increased expression of MeCP2 in mouse cells induces aggregation of pericentric heterochromatin in a dosedependent manner, and the clustering ability is mostly dependent on the MBD  (6 -8). Purified MeCP2 has also been shown to cause compaction of nucleosomal arrays in vitro  (9). Mutations within the X chromosome-located MECP2 gene have been linked to one of the most common human mental retardation disorders in females, Rett syndrome (Online Mendelian Inheritance in Man database no. 321750)  (10). Although missense mutations are mostly accumulated within the MBD (amino acids 78 -162), the majority of nonsense mutations occur predominantly within the TRD (amino acids 207-310).
Images were processed with Adobe Photoshop and ImageJ (http://imagej. nih. gov/ij/). Three-dimensional rendering of image stacks was performed using AMIRA (Visage Imaging Inc. , San Diego, CA) software. Image stacks were analyzed for chromocenter numbers as described in detail before  (14). To evaluate heterochromatin accumulation ability, confocal Z stacks were acquired using an UltraView VoX spinning disc system (PerkinElmer Life Sciences) on a Nikon Ti microscope equipped with an oil immersion ϫ60 Plan-Apochromat NA 1. 45 objective lens (Nikon, Tokyo, Japan) (voxel size, 0. 12 ϫ 0. 12 ϫ 0. 5 m) and a 14-bit electron multiplying cooled chargecoupled device camera (catalog no. C9100-50, Hamamatsu Photonics K. K. , Hamamatsu City, Japan). Z stacks were analyzed using Volocity 5. 5 software (PerkinElmer Life Sciences).
The chromocenter and nucleoplasm were segmented by intensity-based thresholding (Fig. 3 ). Accumulation at chromocenters was calculated from the ratio of the mean gray value at chromocenters to the mean gray value in the nucleoplasm. Accumulation values from both wild-type and PARP-1 Ϫ/Ϫ cells were then normalized to the median accumulation in wild-type cells. To evaluate the binding kinetics of fluorescently tagged MeCP2 and deletion mutants in wild-type and PARP-1 Ϫ/Ϫ cells, a whole chromocenter was photobleached using an Ultra-VIEW VoX spinning disc system (PerkinElmer Life Sciences) mounted on a Nikon Ti microscope equipped with an oil immersion ϫ60 Plan-Apochromat NA 1. 45 objective lens as described before  (23). Quantitative evaluation was performed using ImageJ, and fluorescence intensity normalization and curve fitting were performed with easyFRAP software as described before  (23).
Acknowledgments-We thank  R. Tampé  for  Tris-NTA ,  P. J. Hergenrother  for the poly(ADP-ribose) glycohydrolase inhibitor, and  M. Stilmann  and  C. Scheidereit  for experimental help. We also thank  A. Lehmkuhl ,  A. Gahl ,  M. Milden ,  A. Ackermann ,  C. S. Casas Delucchi , and  K. L. Jost  for help.