dataesr/scientific-paragraphs-categorization huggingface dataset:
Cambridge: Cambridge University Press, Chapter 1, pp. 19-55. J Muller , A. , Mayer, A. , Erb, K. H. et al. (2020). Report on the methodological specification of the spatially-explicit modelling framework. EU H2020 UNISECO ( Understanding and Improving the Sustainability of Agro-ecological Farming Systems in the EU) , Deliverable D4. 1. Available online at: https://doi. org/10. 5281/zenodo. 4555461  J Nakicenovic , N. and Swart, R. (2000). Emissions Scenarios 2000 -Special Report of the  Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. J Niedermayr, A. , Landert, J. , Albanito, F. et al. (2022). Assessing farming systems in transition to agroecology. EuroChoices, this issue. J Poux , X. and Aubert, P. M. ( 2018). An agroecological Europe in 2050:  Multifunctional agriculture for healthy eating.
EU H2020 UNISECO (Understanding and Improving the Sustainability of Agro-ecological Farming Systems in the EU) , Deliverable D4. 2. Available online at: https://doi. org/10. 5281/zenodo. 5576228 J Röös, E. , Mayer, A. , Muller, A. et al. (2022). Agroecological practices in combination with healthy diets can help meet EU food system policy targets. Science of The Total Environment, 847: 157612. J Sattler , C. , Rommel, J. , Chen, C. et al. (2022). Participatory research in times of Covid-19 and beyond: Adjusting your methodological toolkits. One Earth, 5(1): 62-73. J Skaalsveen , K. ,  Ingram , J. and Clarke, L. ( 2019). The effect of no-till farming on the soil functions of water purification and retention in north-western Europe: A literature review. Soil and Tillage Research, 189: 98-109.
J Smyrniotopoulou, A. and Vlahos, G. (2021). Report on the assessment of transdisciplinary tools and methods. EU H2020 UNISECO ( Understanding and Improving the Sustainability of Agro-ecological Farming Systems in the EU) ,  Deliverable   D7. 3. Available online at: https://doi. org/10. 5281/zenodo. 5575927 J Steinitz C. , Arias H. , Bassett S. et al. (2003). Alternative Futures for Changing Landscapes: The Upper San Pedro River Basin in Arizona and Sonora, Washington DC: Island Press. J Wezel, A. , Herren, B. G. , Kerr, R. B. , Barrios, E. , Gonçalves, A. L. R. and Sinclair, F. (2020). Agroecological principles and elements and their implications for transitioning to sustainable food systems A review. Agronomy for Sustainable Development, 40(6): 1-13. J Yang, S. , Cheng, P. , Wang, S. and Li, J.
ACKNOWLEDGEMENTS This project received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020  programme (grant no. 647315  to  Nadine Schibille ). The funding organization had no influence in the study design, data collection, analysis, decision to publish, or preparation of the manuscript. We acknowledge the  Soprintendenza Archeologia, Belle Arti e Paesaggio per le Province di Bergamo e Brescia , the  Soprintendenza Archeologia, Belle Arti e Paesaggio per le Province di Cremona ,  Lodi e Mantova , and especially  Gabriele Barucca ,  Andrea Breda ,  Cristina Longhi ,  Luca Rinaldi , and  Simone Sestito  for their kind assistance and for allowing us to study and publish the material.
This article examines the use of the socio-technical scenarios (STSc) method to explore the future needs of farmers in the adoption of more sustainable farming practices. It also examines the changes needed for advisory services to meet those identifi ed needs. It presents the results from four STSc workshops organised as part of the H2020 AgriLink project. Workshop participants (farmers, advisors, policymakers, researchers), jointly explored future changes towards more sustainable agricultural systems paying special attention to the role of advisory services in these transitions. Despite the diversity of national contexts, common results emerged concerning both the shortcomings of the current advisory systems and directions for improvement. Participants agreed that advising on single aspects would not achieve the required systemic changes; these would require more integrated advisory systems at different levels (improved cooperation and knowledge fl ow), supported by more consistent policies.
What are the main hurdles to a wider use of experiments to study the CAP? The first hurdle that we face is the EU's General Data Protection Regulation or GDPR, that regulates what types of data can be collected on participants in experiments. Implementing a field experiment generally requires the constitution of a database of individual farmers, with information on their location and other possibly identifying information. The GDPR, in its Article 6. 1(e, f), acknowledges that research is a sufficient motive for detaining personal data on people. In its Article 14, The GDPR requires that individuals are informed that their personal data is collected, and can access, modify and erase it. This requirement can prove hard to implement in large field experiments, especially for the control group, which might not be in contact with the experimenters.
Funding information LIFT (' Low-Input Farming and Territories -Integrating knowledge for improving ecosystem based farming ') received funding from the  European Union 's  H2020 research and innovation programme  under grant agreement No  770747  (https://www. lift-h2020. eu/), and  UNISECO  (' Understanding & improving the sustainability of agroecological farming systems in the EU')  under grant agreement No  773901  (https://unise co-proje ct. eu/). Open Access funding provided by  INRAE (French National Institute for Research on Agriculture, Food  and the Environment). Le concept de 'transition juste' gagne du terrain dans les discours de politique au niveau international. Il revêt une importance particulière en ce qui concerne l ' annulation des émissions nettes de gaz à effet de serre et la nécessité de garantir les droits et les responsabilités de tous les acteurs dans les transitions vers des systèmes agricoles agroécologiques.
This is indicated by 'No' in the 'Current whole farm management' column. This most likely reflects the heterogeneity of access to advice on European farms. Statistical differences at whole farm level for digital technologies (TECH) and common management of natural resources (COMM) most likely reflect the larger farm business sizes of adopters. Across the dataset as a whole, adopters were more likely to operate larger farms. This suggests that in broad terms, farmers across Europe have access to a similar range of advisory supports for their farm as a whole. There were more significant differences between adopters, non-adopters and droppers in relation to specific innovations. These varied across the types of innovations pursued, and the stage of innovation. Adopters tended to have more diverse sources of advice at the awareness stage than non-adopters for direct marketing (DMAR) suggesting that lack of advice was a 3.
Data Our sample is derived from multiple data sources. We obtained data from Boardex on US boards of directors for the period from 1990 to 2015 in order to analyze the structure of social exchange between boards. We selected quoted firms and private firms and dropped all other forms of organization (i. e. , universities, charities, clubs, government, etc. ). After this restriction, the initial sample contained 230,431 board members from 384,645 unique firms, resulting in 1,091,118 board members×firm observations. We obtained executive pay data from the ExecuComp database. This database provides compensation data between 1992 and 2015 for 45,649 executives in 3,557 unique firms, resulting in 263,637 executives×firm×year observations. One fourth of this information (77,691) contains data on executive board members; the remaining data concerns top executives who do not sit on boards.
We obtained firm-specific data from Compustat for the period from 1990 to 2016 to enable us to construct several control variables. For example, to control for a firm's revenue, we were able to obtain information for 30,413 firms, resulting in 329,789 firm×year observations. After merging these three datasets, our final sample contained 3,395 unique firms, with 31,852 individual board members and pay for 45,288 executives. Between 1990 and 2015, each year in our final database contained between 2,000 and 2,700 unique firms, 6,000 to 14,000 board members, and 8,000 to 12,000 executives (Table  A1 ).
In order to address this issue, we turn the matrix of control ties between non-isolated firms (≈2500*2500 firms) for each year into a dyadic database (Table  1 ). This generates a database of 163 million dyads. In order to manage it and run the models, we use scarce matrix tools and aggregate identical lines and estimated weighted descriptive statistics and regressions on a reduced matrix of 137,000 lines. This dyadic database enables us to investigate the occurrence of a control tie between two firms. In Column 1, we find that the average probability of occurrence of a control tie between a given firm y and a given firm x is approximately 0. 013%. When the tie existed in t-1, the probability increases to 81% indicating, unsurprisingly, a strong persistence of ties (Column 2). Let us focus on whether a firm will cycle in t when it had an opportunity to do so.
Additionally, the presence of paleochannels and a pinch-out structure of aquitards complicate the local groundwater flow  (Passadore et al. 2012). This domain has been often studied because of the high exploitation of the groundwater resource and the recorded decreasing groundwater levels  (Passadore et al. 2012; Meggiorin et al. 2021 ) and, consequently, some Managed Aquifer Recharge experiences have been there implemented (e. g. ,  Altissimo et al. 2014; Mezzalira et al. 2014; Meggiorin et al. 2019). Therein, the groundwater piezometric head is continuously monitored by a spread information-rich monitoring network. Nevertheless, the longitudinal data has to be preprocessed by removing outliers and by extrapolating normalized average annual variations of the groundwater elevation, a necessary step to highlight local seasonality and avoid clouding the analysis with the mean or the oscillation range of the piezometric head.
Study Area The selected study area is the so-called Bacchiglione Basin (shown in Figure  1 ), located in the Veneto plain (IT) and covering the alluvial plain north of Vicenza. This hydrogeological basin has been already studied in several previous studies  (e. g. , Dal Prà et al. 1976 , 1977; Sottani et al. 1982; Altissimo et al. 1999; Passadore 2008; Marcolongo 2012; Passadore et al. 2012; Sottani and Vielmo 2014; Rinaldo and Passadore 2018; Meggiorin et al. 2021 ) because of (i) the complex structural system, (ii) its historical water abundance, (iii) its quantitative depletion over the years, and (iv) the widespread monitoring network that continuously measures the groundwater elevation in several control points. Thus, within this study area, a sizeable dataset is available, particularly suitable for statistical analyses.
Data and Methods The proprietary dataset used for the following analysis corresponds with the one used in  Meggiorin et al. (2021) , where more details on the monitoring network are available. As a brief summary of the available data, the piezometric head is measured in 102 monitoring points with a sampling interval of 1 or 3 hours and available recordings have different lengths: ranging from 3 months to almost 14 years. In addition to this heterogeneous recorded time period, the dataset is affected by missing values and outliers, as usual for real case studies. The firsts are present because of (i)  different sensors' installation times, (ii) sensor breakdowns, or (iii) particularly dry periods for which the sensor is not located deep enough, thus being suspended above the water table. The latter can be present due to sensor malfunctions or to the pumping effect of nearby wells.
Outliers have to be removed for applying the statistical analyses and, in the present study, the same procedure as in  Meggiorin et al. (2021)  has been applied. Starting from that dataset, a further sensor-specific documental analysis led to removing 17 points because too close to pumping wells, likely affecting the recorded piezometric head. Indeed, this study aims to strengthen the conceptual model of the groundwater system, therefore focusing on the natural groundwater level trend. Records potentially affected by nearby pumping wells could mislead the analysis. Furthermore, to achieve robust results, records shorter than two years or with a majority of missing values within the recorded period have been removed from the dataset. In conclusion, the dataset includes 63 time series. Finally, the dataset preparation for the PCA involves that time series have been processed as follows: (i) original time series are centered around the 0 value, (ii) records have been monthly averaged in order to have a uniform monthly timestep, (iii) monthly time series are normalized to range between -1 and 1, and (iv) typical Yearly Hydrographs are calculated by averaging records for each month (see Figure  2 ).
Briefly, PCA aims to orthogonally transform the dataset by identifying the smallest number of principal components (PCs) that are able to account for most of the original dataset variability  (Gunasekaran and Kasirajan 2017). The first PC accounts for most of the data variability and the following PCs explain the highest left variability, being orthogonal to all previous components. Thus, first few evaluated PCs explain most of the variability of the original dataset: hopefully first two or three components entail more than 70-80% of the original dataset variability. If this is the case, useful insights can be observed by PCs scatter plots, called factorial plans, showing how and if observations group along with first PCs. The remaining PCs contain much less information, and they can be neglected  (Ribeiro et al.
Results The PCA is applied to the dataset of yearly hydrographs. The first two identified PCs explain more than 68% of the dataset variability and they represent 'Reference Hydrographs' showing peaks and minima of the groundwater elevation. Each field hydrograph is a linear combination of the two Reference Hydrographs and their weights are visible in the factorial plan. The PCA results are reported in Figures  3  and 4  respectively as Reference Hydrographs and as Factorial plan. The first PC, explaining most of the dataset variability, is a groundwater hydrograph showing maximum groundwater levels between December and March while minimum levels are between July and September. On the contrary, the second PC has the highest values between April and June and the lowest levels between October and November.
These two Reference Hydrographs explain most of the original dataset and their weights for each field hydrograph are shown in the Factorial Plan, Figure  4. In this graph, observations can be grouped in clusters being similarly explained by main PCs, therefore being similarly characterized by the Reference Hydrographs. Nevertheless, in this specific case, the factorial plan is not particularly enlightening because clusters are not easily identifiable. Consequently, the dataset has been split into two subdomains depending on the hydrological basin, which in the study area is strongly connected with the underground hydrogeological basin: the Leogra-Timonchio-Astico rivers (LTA) at West and the Brenta river at East. Figures  5  and 6  show the results of the PCA for the two sub-datasets. Particularly, it is evident how the two domains have different Reference Hydrographs: the first PC has the maximum and minimum respectively in December and July for the LTA domain, in March and August for the Brenta domain.
Therefore, in both datasets, the period with minimum groundwater levels is within the summer season, while the maximum is in early winter for the LTA while it is in late winter for the Brenta domain. The second PC also differs in the two datasets: the LTA Reference Hydrograph shows only one peak in October and one minimum in April, on the contrary, the Brenta hydrograph shows two minima and maxima along the year. Factorial plans for the two sub-datasets are shown in Figure  6  and, in this case, the visual clustering comes relatively easy because of the great spread of the observations within the graph. From these results, 6 clusters are identified within the Brenta domain and 10 within the LTA domain. The same clusters are plotted above the original factorial plan calculated in the first overall analysis (visible in Figure  7 ) showing how the clusters were already present in such analysis, even if it was more difficult to identify them.
A visual check of the clustering has been carried out by observing grouped hydrographs, results are visible in Figures  8  and 9. The overall comprehension is that the PCA well identifies the clusters depending on the number, location, and lengths of groundwater level peaks and minima. For example, for the LTA sub-dataset, the two hydrograph aspects that are most relevant in differentiating the clusters are (i) the location and length of the maximum groundwater level and (ii) the groundwater trend during the fall rise, between October and December. On the contrary, for the Brenta sub-dataset, the most relevant hydrographs' characteristics are (i) the maximum magnitude and location and (ii) the groundwater trend during the early fall season.
Discussion This study applied the PCA to analyze the available groundwater level dataset to identify hydrogeologically homogeneous areas. Specifically, the PCA examines the average yearly hydrograph of all control points within the study area in order to cluster them and identify areas characterized by similar hydrographs. Normalized average yearly hydrographs were preferred to the original records (with observations roughly between 2005 and 2019) because they allow having a uniform dataset without missing values and focused on the average groundwater level seasonality. The main hydrogeological results of this analysis are reported in Figures  8  and 9. Clustered yearly hydrographs show how the analysis is able to differentiate such hydrographs depending on their peculiar trend. On a few occasions, a later visual inspection would lead to joining clusters or moving a time series from one cluster to another.
Nevertheless, this anomaly has been further investigated in order to understand the hydrogeological reason for such differentiation. A visual comparison of the original time series (see Figure  12 ) highlights how the investigated control point recorded a trend like the time series grouped in cluster 4-Br. However, the different recorded time periods are evident, much shorter for point 168. Indeed, the investigated control point monitored the groundwater level between 2016 and 2018 but even in this period the groundwater level recorded in 168 is more associable to cluster 4-Br than to 2-Br. Thus,    This anomaly shows a limit of the PCA for studying the groundwater system. Indeed, even if the data has been preprocessed to have a homogeneous dataset, the different lengths of the original time series could affect the clustering due to potential hydrological changes over the years (e.
Conclusions The present study proposed and tested the application of PCA to a regional scale dataset of the groundwater elevation in order to achieve insights into the conceptual model of the monitored aquifer system. The results clearly show how the analysis made it possible to highlight the correlation between the groundwater regime and some hydrogeological and environmental factors. The comparison between two different hydrogeological systems (Brenta and LTA) gave the opportunity to verify the sensitivity of the method with respect to the hydrogeological complexity of the area. The PCA returned clear results, with neat driving factors, for the orderly Brenta domain while results are more multifaceted for the complex LTA domain. This analysis highlighted indeed how the LTA system is far from the homogeneous unconfined system described in the literature and how it hides local complexities.
Furthermore, the study of a few anomalies in the clustering results highlighted (i) the peculiarities of some points, (ii) the relevance of records' different lengths that can affect the statistical analysis. Nevertheless, in this study, the investigation of such clustering anomalies spotted a change in the hydrogeological cycle over the years (affecting the groundwater yearly hydrograph) and this is an interesting side result of the PCA application. As for the future prospects, the achievable insights applying the PCA to many different large-scale monitoring networks of the groundwater level could unveil relationships between catchment properties (i. e. , paleochannels and rivers dispersion) and the dynamic behavior of groundwater. The possibility of building a very large database of such relationships is foreseen to quickly classify each new observation point on the basis of.
Campionamento e numerosità campionaria Il campione, non probabilistico, sarà rappresentato dai bambini ricoverati nelle UTIP dell'AOU di Parma e dell'Istituto Gaslini nel periodo in cui si svolgerà lo studio. I criteri di inclusione e di esclusione sono rappresentati in tabella 1. La numerosità campionaria è stata individuata grazie a un'analisi di potenza a priori utilizzando il software G*Power. È stata impostata potenza attesa = 0. 80, alfa = 0. 05 e effect size moderato (δ = 0. 3) che ha rimandato una numerosità campionaria prevista di 84 soggetti.
La c. d. legge Puglia, pur essendo adottata per far fronte alle esigenze umanitarie, aveva l'obiettivo dichiarato di controllare la frontiera marittima mediante l'impiego di forze militari «per esigenze connesse con il fenomeno dell'immigrazione clandestina nelle medesime province». Inoltre, la legge istituiva a cura del Ministero dell'Interno «tre centri dislocati lungo la frontiera marittima delle coste pugliesi per le esigenze di prima assistenza a favore di gruppi di stranieri privi di qualsiasi mezzo di sostentamento, in attesa di identificazione o espulsione». La tutela dei diritti costituzionali dei migranti ai confini Inoltre, riguardo alla possibilità di uscire dall'hotspot, la circolare precisa che «salvo il verificarsi di afflussi eccezionali che impongono l'adozione di iniziative diverse, la persona può uscire dall'hotspot solo dopo essere stata foto-segnalata, concordemente con quanto previsto dalle norme vigenti, se sono state completate tutte le verifiche di sicurezza nei database, nazionali ed internazionali, di polizia all'interno degli hotspot» 20.
Viene, pertanto, prevista una controversa  (Thym 2022 , Cornelisse 2022 ) procedura di accertamento alla frontiera (pre-entry screening)  48  , applicabile nei confronti di quanti siano sbarcati a seguito di un'operazione di salvataggio o di quanti giungano alle frontiere esterne sprovvisti dei requisiti per l'ingresso, e ivi richiedano la protezione internazionale ovvero siano fermati in relazione all'attraversamento non autorizzato via terra, mare o aria della frontiera esterna di uno Stato membro (art. 3). Sarebbero sottoposti a screening anche quanti, sottrattisi ai controlli di frontiera, siano successivamente identificati nel territorio di uno Stato membro (art. 5). Si prevede che la procedura, finalizzata all'identificazione e all'effettuazione di verifiche sulla sicurezza e sulle condizioni di salute, al rilevamento delle impronte digitali e alla registrazione nel database EURODAC, nonché ad assicurare un più rapido avvio e svolgimento delle procedure per il riconoscimento della protezione internazionale ovvero di rimpatrio, sia espletata ordinariamente entro 5 giorni (una proroga fino ad un massimo di ulteriori 5 giorni è consentita in circostanze eccezionali, qualora sia necessario sottoporre, contemporaneamente agli accertamenti, un numero sproporzionato di cittadini di paesi terzi, mentre sono previsti 3 giorni dal fermo nel caso in cui la persona sia stata fermata sul territorio nazionale).