dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenies Phylogenetic trees were extracted from the Open Tree of Life  (Hinchliff et al. , 2015; Rees & Cranston, 2017) , using the interface provided by the R package 'rotl' (v3. 0. 12; Michonneau et al. , 2016; OpenTreeOfLife et al. , 2019). We calculated tree branch length  (Grafen, 1989)  and generated a phylogenetic correlation matrix to include in all our phylogenetic multilevel meta-analytic models (Figure  1 ). We assessed the phylogenetic signal in our meta-analysis based on the proportion of variation explained by the phylogeny (I 2 phylogeny ;  Cinar et al. , 2022).
We compared models with different constraints in the parameters of the variance-covariance structure in Equation 3 to assess the strength of evidence for heterogeneous variances and correlations among traits (see results in Tables  S2  and S4 ). We fitted these trivariate meta-analytic models in the 'metafor' R package  ('rma. mv' function; v3. 4. 0; Viechtbauer, 2010)  using maximum likelihood and compared models using AIC (Akaike Information Criterion;  Burnham et al. , 2011). We then calculated a ΔAIC value for each model (i. e. the difference in AIC between a given model and the model with (1) the lowest AIC) and used this value to assess the strength of evidence for a given variance-covariance structure. We fitted models with the following constraints in the variance-covariance structure: (i) Single variance across traits and zero covariances: y i = ⎡ ⎢ ⎢ ⎣ LD CS NF ⎤ ⎥ ⎥ ⎦ + ⎡ ⎢ ⎢ ⎣ i-LD i-CS i-NF ⎤ ⎥ ⎥ ⎦ + ⎡ ⎢ ⎢ ⎣ t-LD t-CS t-NF ⎤ ⎥ ⎥ ⎦ + v y + a l + h w , (2) i-LD i-CS i-NF ∼ N ⎛ ⎜ ⎜ ⎝ ⎡ ⎢ ⎢ ⎣ 0 0 0 ⎤ ⎥ ⎥ ⎦ , ⎡ ⎢ ⎢ ⎣ 2 -LD 2 -CS 2 -NF ⎤ ⎥ ⎥ ⎦ ⎞ ⎟ ⎟ ⎠ , (3) t-LD t-CS t-NF ∼ N ⎛ ⎜ ⎜ ⎝ ⎡ ⎢ ⎢ ⎣ 0 0 0 ⎤ ⎥ ⎥ ⎦ , ⎡ ⎢ ⎢ ⎣ 2 t-LD p LD-CS t-LD t-CS p LD-NF t-LD t-NF 2 t-CS p CS-NF t-CS t-NF 2 t-NF ⎤ ⎥ ⎥ ⎦ ⎞ ⎟ ⎟ ⎠ , (4) v y ∼ N 0, 2 v , (5) a l ∼ N 0, A 2 a , ( 2 t-LD = 2 t-CS = 2 t-NF ; and all p = 0 (ii) Compound symmetric variance-covariance matrix: 2 t-LD = 2 t-CS = 2 t.
Within-and between-breeding season differences in phenology and life-history traits Urban and non-urban populations may differ in both within-and between-breeding season variation in lifehistory traits. However, differences in variation for these two temporal scales are likely generated by contrasting ecological and evolutionary processes. To disentangle processes operating at these two temporal scales, we performed additional meta-analyses including (i) urbannon-urban comparisons within breeding seasons (k = 363 comparisons from 47 studies in the original dataset with effect sizes per year; Model 5) and (ii) urban-non-urban comparisons between breeding seasons (i. e. combining all within-breeding season estimates from a study; k = 36 comparisons present in the original dataset, plus 67 additional comparison calculated from within-breeding season estimates; see below).
Sensitivity analyses We assessed the robustness of our results with several complementary analyses. First, we re-ran the trivariate lnRR model (Model 2; Table  1 ) using Hedges' g  (Hedges, 1981)  with heteroscedastic population variances as the response variable (Model 8; Table  1 ; i. e. 'SMDH', calculated using the R function 'escalc' in the 'metafor  ' R package (v3. 4. 0; Viechtbauer, 2010) ). In addition, we assessed the robustness of the lnCVR results by re-running the trivariate lnCVR model (Model 4; Table  1 ) using lnVR as the response variable (i. e. the logarithm of the total variation ration;  Nakagawa et al. , 2015 ; Model 9; Table  1 ). Last, we used an alternative approach that directly models the log of the phenotypic standard deviation (lnSD) to assess differences in phenotypic variation between urban and non-urban populations (equation 18 in  Nakagawa et al.
R E SU LT S After systematically inspecting 1166 studies published between 1958 and 2020 (Figure  S1 ), our meta-analysis included 399 urban-non-urban comparisons for three bird life-history traits: laying date (k = 151 effect sizes, n = 32 studies), clutch size (k = 119 effect sizes, n = 42 studies) and number of fledglings (k = 129 effect sizes, n = 48 studies) (Figure  1 ). This dataset included bird species, with most studies located in the northern hemisphere (Figure  1c ).
DI SC US SION We compiled a global dataset of bird life-history traits for paired urban and non-urban populations of the same species to assess how urban living is related to changes in phenotypic means and variation for breeding phenology, reproductive effort and reproductive success. A phylogenetically controlled multilevel meta-analysis of this dataset confirms a well-documented effect of urbanisation on mean phenotypes: urban bird populations lay earlier and smaller clutches than their non-urban counterparts. This model, however, also reveals correlated responses to urbanisation across life-history traits: for example, the earlier the laying date in urban populations, the smaller the difference in clutch sizes between habitats. Our study goes a step further than previous meta-analyses in urban ecology by explicitly investigating how urbanisation could impact phenotypic variation.
The NFO community in EPOS The NFO community in Europe originated under the FP7 European project NERA -Network of European Research Infrastructures for Earthquake Risk  Assessment and Mitigation (2010-2014) , when a networking initiative started to coordinate efforts of different institutions owning dense observatories on faults, culminating in sharing of technology know-how and data analysis best-practice. The success of the initiative led to the NFO community becoming a Thematic Working Group of the EPOS-PP -Preparatory Phase (EU project funded under the FP7 programme -2010-2014). In the EPOS-IP project (EPOS Implementation Phase, funded under the Horizon 2020 programme -2015-2019) the NFO community was officially recognized as one of the Thematic Core Service (TCS). During EPOS-IP, the current foundations for our community were built, through the definition of a Consortium Agreement that comprised a Legal, Governance and Financial framework.
Horizon 2020 programme, 2017-2020) to develop and apply advanced strategies and techniques of data analysis for microseismicity. Today, the NFO community is a TCS of the EPOS delivery framework. The NFOs comprise six members and one observer. They span different tectonic regimes, different areas and different space scales over Europe (Figure  1 ), all of them at sites of elevated seismic hazard. They include plate boundary systems at the Marmara Sea and the Corinth rift. In mountain settings, NFOs monitor the Alto Tiberina and Irpinia faults in the Apennine Mountain range, the Vrancea fault in the Carpathian Mountains, the Valais in the Alps and the Slovenian Karst region in the Dinarides. They look at diverse faulting mechanisms (strike-slip, normal and thrust), high to low angle, shallow to deep faults, as well as regions with fast and slow strain rate accumulation.
The TCS Delivery Framework The mission of the TCS NFO is to provide a sustainable platform for coordination between each European observatory, sharing data and products promoting best practice that ultimately will foster breakthrough research on faulting and earthquakes. his effort is regulated through the Consortium Agreement (CA), representing the NFO reference framework, specifying the relationship between Parties, their rights and obligations, as well as providing organisational, managerial and financial guidelines. The TCS portfolio comprises data access, visualization and processing tools, testing facilities and transnational access. Data access from NFOs follow existing EPOS TCS services where they exist for seismic and GNSS datasets (raw data and high-level products) and provide new NFO-specific access services otherwise such as for Vp/Vs time series, high resolution earthquake catalogues and geochemical data.
To process multi-disciplinary time series and intersect information appearing in different records, NFOs have developed tools that will help in providing improved models of transient processes in/around fault zones and in building advanced databases and catalogues of such processes. These tools will be in the future included in the Virtual Laboratory service. Also, the NFO community has developed a testing facility, built on real-time and offline high-resolution data, to foster the development of next generation methodologies and software for real-time monitoring of faulting processes. For physical access, NFOs will open their on-field laboratories to design new experiments, to test new instruments, to validate methodologies and real-time software, approaching breakthrough scientific questions.
Data and products delivered by the TCS are intended to provide the Earth Sciences scientific community with a fundamental dataset that can accelerate to advancement in understanding fault system behaviour. Data and high-resolution data products will provide clues to understand the forcing mechanisms associated to earthquakes (fluid migration, creeping), the preparation phase of large earthquakes, the partition of the slip in seismic and aseismic contributions, as well as markers for changes in the mechanical properties of the faults. The NFOs represent ideal testbeds for generating expertise on data integration, for creating tools for the next generation of multidisciplinary research, advanced routine data analysis, also grounded on machine learning and artificial intelligence, and data visualization, for developing new experiments and survey through transnational access.
These integrated datasets alongside provision of virtual and physical access to NFOs is expected to create positive feedback to solve the puzzle of faulting and associated deformation. The NFO delivery framework also facilitates technological and knowledge transfer for scientific and technical training, as well as public outreach, at different levels: general education of public on natural hazards and seismo-tectonic processes, higher education (summer schools, masters and postgraduate research) on advanced instrumentation techniques, multi-parameter analysis of crustal processes. Bringing together multidisciplinary data and expertise at a single site to be processed and integrated, will put important pieces in the puzzle of earthquake mechanics. Finally, besides the monitoring of the faults before the occurrence of main events, the dense NFO networks can also improve operational actions conducted in the aftermath of a large earthquake, such as Earthquake Early Warning (EEW) and detailed, fast ground shaking and damage characterization during the on-going rupture on the fault.
The Near Fault Observatories in Europe In the following, we introduce the six NFOs that have formally signed the CA. The six NFO infrastructures are indicated in Figure  2. NFOs are usually operated by a single National organisation but can also be represented by multiple National and European organisations. The complexity beyond National Research Infrastructures (NRIs) is managed at the NFO level. NFOs, in line with other TCS service providers, fully adopt the EPOS data policy and permit open and free distribution of their own data and products through the EPOS services, on behalf of the data owners (Suppliers), and in compliance with FAIR principles. In future, new NFOs can be expected to join the community. Recently the Slovenian Karst NFO has joined the Consortium as an Observer; a status formally recognised by the community, allowing potentially new NFOs to participate in the CA board meetings and initiatives.
, 2021]. Furthermore, two multiparameter sites are operated in CRL (see Figure  2 ). The first is RIZA which is equipped with borehole three-component strainmeter, rainmeter, borehole water-level and atmospheric pressure sensors. The second site is MOKI, where the multparameter instruments are borehole three-component strainmeter, tide gauge, borehole water-level and atmospheric pressure sensors. Seismological data are available through the European Integrated Data Archive (EIDA) nodes at RESIF and NOA  [Evangelidis et al. , 2021]. EIDA has declared the virtual network identifier _NFOCRL for all seismic stations within the CRL perimeter. CRL also distributes scientific products, such as phase and focal mechanism data NKUA, NOA and UPAT. GNSS data and positioning solutions are now available at the CRL portal (https://crlab.
eu) and will migrate to the EPOS data portal. The use of the freely available CRL data highlighted that most seismicity is clustered, with the frequent occurrence of swarms, as the 2001 Agios Ioannis  [Pacchiani and Lyon-Caen, 2010] , the 2013 Helike  [Kapetanidis et al. , 2015 [Kapetanidis et al. , , 2021;; Kaviris et al. , 2017]  and the 2015 Malamata  [De Barros et al. , 2020]  ones. Geodetic studies, from permanent and campaign GNSS network measurements and InSAR, identified the deformation sources of the Aigion  [1995]  and  Efpalio [2010]  earthquakes, as well as slow aseismic shallow slip [Elias and  Briole, 2018]. The recent 2020-2021 seismic crisis was studied by the CRL research group  [Kaviris et al. , 2021]. In the latter case, the analysis of the CRL multiparametric data significantly contributed to understand the seismogenic process.
Combined seismological and geodetic data were considered in order to constrain the geometry and kinematics of the structures that hosted the major events of the 2020-2021 seismic crisis. In addition, the significant role of CRL tide-gauge data, analyzed along with seismological and GNSS recordings, is proven in ongoing research that aims to identify the rupture process of the largest event of the sequence that occurred on 17 February 2021. The wealth of the multiparametric CRL data led to the in-depth knowledge of the seismogenesis in the Western Gulf of Corinth. The dual behaviour of certain fault segments, subject to transient creep and to pore pressure diffusion, has been identified in the WGoC. The detailed analysis of multiparametric CRL data also led to the identification of a brittle, highly fractured layer in depths between 6 km and 9 km, where the major normal faults of the CRL area are rooting  [Lambotte et al.
, 2010]. The average inter-station distance within the inner part of the infrastructure is less than 10 km. All stations are equipped with a strong-motion accelerometer and a weak motion sensor, the latter being a short-period, a broadband velocimeter or an accelerometer with lower full-scale to record both strong and weak motions associated with earthquakes and ambient noise. ISNet provides real-time data with a controlled delay, allowing the infrastructure to be the Italian backbone for testing EEW systems  [Satriano et al. , 2011]. The seismic network is complemented with 9 additional broadband stations from the national network, 15 GNSS stations, managed by INGV, and two seismic arrays of 5 six-component stations at the centre of the network. Recently, a 1. 1 km fibre optic cable (FibIR) has been installed near the emergence of the main fault that generated the 1980 earthquake, and it is now sensed by a DAS, in the frame of the IrpiDAS survey.
About 200 digital strong motion accelerometers are operated by KOERI as dense urban network in and around Istanbul (Rapid Response and Early Warning System). KOERI also operates 5 sea-floor multi-instrument observation systems in the Sea of Marmara, actually deserving renovation, and acts as the National Tsunami Warning Centre for Turkey under the ICG/NEAMTWS initiative. AFAD's Earthquake Data Center System of Turkey (TDVMS) is a system which pairs up all the earthquake data obtained from seismic stations all around Turkey and shares these data via its web portal. It also operates like BU-KOERI and run more than 1000 BB and SM stations. GCM controls more than 150 Continuous GNSS stations, including other geodesic networks (e. g. gravity, sea-level monitoring). At the same time, many international efforts have been posed to better define the earthquake hazard pattern of the region.
The achieved results can be fruitfully compared with the available independent in-situ measurements (e. g. , GPS data, Strainmeter) providing additional constrains and details  [Diao et al. , 2016; de Michele et al. , 2017; Aslan et al. , 2018; Martínez-Garzón, 2021]. In this framework, a core study group has been established in the consortium of the European Commission funded MarSite (New Directions in Seismic Hazard Assessment through Focused Earth Observation in the Marmara Supersite) project  (2012) (2013) (2014) (2015) (2016). MarSite organized as the demonstrator for integrating satellite and in-situ observations and for providing integrated access to data until the end of the project. From the end of the MarSite project, most of the activities and related data and scientific products, were reorganized and harmonised within the Marmara Near Fault Observatory, coordinated under EPOS support.
Permanent and temporary sets of data have been standardised following communal open access sharing rules and new web services have been implemented in the EPOS framework. Not all the existing infrastructures have been already harmonised; this is a long process deserving time to be pursued. For sure NFO community represents an optimal opportunity to create a common scientific environment primarily finalised to gain valuable information about the off-shore unbroken segments of the region to be shared with decision-makers to improve the hazard maps of the Marmara (specially Istanbul) and their risk-plans. At the same time, NFO novel platforms for data exposure and sharing will represent an occasion as well to provide to every kind of users with all the available data and scientific products generated by such an important suite of multidisciplinary monitoring and research infrastructures.
, 2014; Tohoku earthquake, Kato et al. , 2012 and Parkfield, Veedu and Barbot, 2016]. Why a fault patch would switch from one mode of slip to another one runs contrary to the standard theory. Thus, these observations are forcing a revolution in our way of thinking about how faults accommodate slip. However, the interaction between creep, slow and regular earthquakes is still poorly documented by observation. TABOO is currently collecting unique data needed to address such questions; by illuminating the physics that allows for both seismic and aseismic slip on a single fault patch, will have important implications for seismic hazard and risk assessment globally.
Then, GNSS antennas, radon monitoring system, meteorological stations, electromagnetic stations and atmospheric ionization monitoring systems complement the infrastructure  [Mărmureanu et al. , 2021; Toader et al. , 2021]  Such a high-resolution multidisciplinary monitoring and research infrastructure consents the availability of a large variety of data to better understand the possible extension at depth of the signature of active tectonic processes. However, although deep, the major seismic events occurring in Vrancea are not only felt in a very large area and Cernavoda  [Mărmureanu et al. , 2021]. Having the NFO community the leadership in EWS services, furnishes the great opportunity to continuously test and update innovative schemes and benchmarking systems such as the communication freely to users through social platforms, currently counting in Romani ~14000 users.
, 2021]. In order to facilitate the NFO data identification, discovery and download, together with the Seismology TCS, we created the NFO Virtual Networks (VN), allowing the user an easier and more comprehensive way to discover and download the data. Now, all the 6 NFOs have a VN code identifying the infrastructure. The same concept is being developed for the geodetic data, working in close collaboration with the GNSS TCS. The community is currently inserting/uploading the NFO GNSS station information (and metadata) into the nodes of the newly implemented GLASS open-source platform. Since the NFO also provide additional data and products that are not provided by other TCS, these NFO Specific Data, defined as raw data and high-level data products originally not having a standard (e. g. , format, unit of measurements, metadata), the NFO community built a dedicated portal named FRIDGE, standing for Federated Specific Data Gateway (http://fridge.
ingv. it). NFO Specific Data are the continuous raw geochemical data (e. g. , radon counts, CO 2 flux time series), with associated meteorological parameters. High level data products cover diverse disciplines characterizing the NFOs, such as seismology (e. g. , vp/vs time series, various generation and typology of earthquake catalogues, velocity and attenuation models, historical earthquakes), geodesy (e. g. , strain-rate time series) and geology (e. g. , fault inventories, geological maps). Thus, the process of distributing NFO specific data and products included not only planning FRIDGE from the computer science perspective but also the identification of new standards. An example of this activity comes from the work we carried out and/or currently undergoing, together with the volcanological and seismological communities (always within the specific harmonization groups), in order to standardise geochemical data and seismological data products, respectively.
FRIDGE is then a unique and coordinated access for NFO Specific Data pointed by both the EPOS-ICS-C platform and services as well as by the indigenous users. From a technical point of view, FRIDGE has been thought as a federator exposing HTTP API for discovering and querying all the available NFO datasets and combining replies for the user (ICS-C or anybody) as well as for hosting a Virtual Laboratory, an engagement and knowledge sharing instrument (see Figure  3 ). The federator is implemented in a modular way through Tornado Framework, a python web server composed by a main module and different plugins for each web service implemented by the single NFOs. This allows an easy development of new future services while reducing its maintenance. The federator exposes a set of APIs, through which it is possible to query the web services asking for the data provided by the NFOs.
When a user performs a request based on geographical coordinates, the federator addresses and redirects it to the corresponding NFO. The federator exposes their APIs also to the Fridge web portal that offers an alternative graphical user interface (GUI) to search and download data and data products, and view time series and graphical maps. The availability of such a diverse kind of time series gives to the scientific stakeholders the novel opportunity to start looking together at interdisciplinary data and derived scientific products collected in the same area and at the same time. These actions, opening to the possibility of connecting different observations to the same phenomena, can also give the rise to a new generation of scientists capable right from the beginning to consider multidisciplinary data.
CREW Consisting of dense, modern and low-latency infrastructures, the NFOs are also ideal sites to process real-time data, making them test-beds for experimenting Earthquake Early Warning (EEW) systems. EEW systems can be used to activate security actions and mitigate the seismic risk during an occurring earthquake. In this context, the community has developed the platform CREW -EU Testing Centre Early Warning & Source Characterizationto test state-of-the-art systems, software and platforms, to provide a fair comparison of methodologies, based on community-based performance criteria, providing a complete report of the results. From a technical point of view, CREW (Figure  4 ) is structured in virtual machines, such that each system can be fully configured and customized according to its architecture by the relevant experts during the initial set-up and tuning phase that precedes the formal testing.