dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank all presenters and participants of the CUAHSI cyberseminars for their insightful presentations and the rich discussions that followed, and partly motivated this opinion paper. They were  Ben Abbott ,  Alexandra Arènes ,  Emma Aronson ,  Bhavna Arora ,  Vanessa Bailey ,  Jerad Bales ,  Holly Barnard ,  Laurie Boithias ,  Mikayla Borton ,  Isabelle Braud ,  Eoin Brodie ,  Nicolas Brüggemann ,  Goi Ho Chin ,  Louis Derry ,  Dipankar Dwivedi ,  Sylvie Galle ,  Ian Giesbrecht ,  Ciaran Harman ,  Sibylle Hassler ,  Steven Holbrook ,  Susan Hubbard ,  Andrew Ireson ,  Kathi Jo Jankowski ,  Xiaoxu Jia ,  Lixin Jin ,  Esteban G. Jobbagy ,  John Kominoski ,  Praveen Kumar ,  Melissa Lafreniere ,  Tanguy Le Borgne ,  Li Li ,  Gunnar Lischeid ,  Susanne Liebner ,  Philip Marsh ,  Holly Michael ,  Oliver Mogase ,  David Moulton ,  Michelle Newcomer ,  Xinhua Peng ,  Daniele Penna ,  Cristy Portales Reyes ,  Antonello Provenzale ,  Boqiang Qin ,  William Quinton ,  Victoria Quiroga ,  Matt Ross ,  Cody Ross ,  Muddu Sekhar ,  Chaopeng Shen ,  Heidi Steltzer ,  Kathe Todd-Brown ,  Laura Toran ,  Mark Torres ,  Marie Toussaint ,  Tao Wen ,  Stan Wullschleger ,  Fan Zhang ,  Yangjian Zhang ,  Liang Zhang , and  Sam Zipper.
or g/record/5153305 Macrosheds US Hydrobiogeochemistry Vlah et al. , 2022 https://cuahsi. sh inyapps. io/ma crosheds/ CHOSEN US Hydrology Zhang et al. , 2021 https://zenodo. or g/record/4060384 CAMELS-Chem US Hydrobiogeochemistry Sterle et al. , 2022 https://drive. goo gle. com/drive/fo lders/1AF37U3 jXW8nxIe195bb2 nN2HDpDsdKVr WHONDRS Worldwide Hydrobiogeochemistry Stegen and Goldman, 2018 https://ess-dive. lbl. gov/ GNIR Worldwide Stream water isotopes International Atomic Energy Agency, 2012 https://www. iaea. org/services/netw orks/gnir National dataset Canada Stream water isotopes Gibson et al. 2021 of streamflow isotopes Data link.
https://www. smhi. se/data/utforskare n-oppna-data/ FLUXNET Worldwide Surface-atmosphere fluxes of carbon, water and energy Pastorello et al. , 2020 https://fluxnet. org/data/ LIAISE Spain Land surface interactions Boone et al. , 2019 https://liaise. aeri s-data. fr/products/ SoDaH Worldwide Soil organic matter Wieder et al. , 2021 https://lter. github. (continued on next page) B. Arora et al.
, 2001). To test and assess the accuracy of this new empirical model of the solar corona, we propagate the obtained solar wind properties with a 3D MHD model from 0. 1 au to 1 au. The combination of the WL boundary condition and the 3D MHD propagator forms the HelioCast model. In Section 2, we present the 3D MHD model's equation and the characteristics of the simulations. In Section 3, we precisely describe how the ambient solar wind boundary condition is created from C2 WL measurements. We use OMNI and Ulysses data to relate the solar wind properties to the angular distance to the HCS at all latitudes. Section 4 compares the performance of the model depending on the variation of a single parameter d, the location of the transition from slow to fast wind going away from the HCS. Both point-by-point metrics and event-based comparisons are performed, allowing us to assess the best value for the parameter d.
For comparison, we plot the HCS location predicted by a PFSS model based on the WSO magnetic map of CR2200. We see that both curves are close (with a confidence score of 84. 6%, see  Poirier et al. , 2021 , for more details) and that we can use the SMB position as a proxy for the HCS. A statistical comparison with magnetic sector measurements made at 1 au further showed a good correlation between timings when the polarity switches sign and when the SMB is crossed  (Poirier et al. , 2021). To build our solar wind model, we focus on the relation between the wind speed and the angular distance to the SMB. Figure  2  shows the relation between the angular distance (in degrees) d and the wind speed using two datasets. In blue, we show the computed 2D histogram of Ulysses data points, with 200 Â 200 bins and normalized between 0 and 1 for each vertical d bin, during the latitudinal scan of 2007.
The distance d to the HCS is computed using SMB reconstruction from LASCO/C2. In orange, we show the OMNI data histogram (taken at 1 au in 2018, 2019, and 2020) with the SMB computed with the same method. We see that the wind speed follows roughly a step function, going from 300 km/s up to 750 km/s. We assume that the solar wind has reached terminal velocity at 1 au and that it is not accelerated beyond (Ulysses orbit is between 1. 3 and 2. 5 au during this period). Figure  2  shows also that there is a large variability or uncertainty in the position of the transition between slow and fast wind streams as a function of the angular distance d. In black, we plot two different models using the logistic function  V. Réville et al. : J. Space Weather Space Clim. 2023, 13, 11  each model/d value, we want to express the remaining hydrodynamical quantities as a function of the wind speed at 0.
The likelihood function we seek to maximize is then given by: p NjV ; h ð Þ¼ Y i 1 ffiffiffiffiffiffiffiffiffiffi 2pr 2 p e À 1 2r 2 jN i ðV iÞÀN ðV i ;hÞj 2 ; ð9Þ with h = [N 0 , V 0 , a] T the parameter vector to fit. The maximum likelihood is obtained for N 0 = 1600 cm À3 , V 0 = 345 km/s and a = 2. 8. For the wind proton temperature, more discussion is necessary, as its evolution with distance to the Sun is complex and still not completely understood. In Figure  4 , we present the proton temperature of the two datasets previously used, as well as the proton temperature measured by Parker Solar Probe (PSP) during encounter 8, between 16 R and 100 R. The PSP proton temperature corresponds to the L3 product of the SWEAP/ SPAN-i obtained through the moment of the velocity distribution function (see  Kasper et al.
The amplitude of the maximum velocity, the value of the low speeds, and to some extent the occurrence of the peaks are affected by the choice of d. For d = 10°, most HSSs are detected, but their amplitude is too high at the beginning of the interval while improving over the six-month period. The slow wind speed predicted by the model is also too high, around 400 km/s when the data shows a slow wind plateau of around 300 km/s. Conversely, for d = 30°, the Earth stays most of the time in 300 km/s slow wind, and only a few high-speed streams do emerge in the solution. Finally, the intermediate value d = 20°, shows a better agree-Fig. 5. Proton temperature at 0. 1 au as a function of the wind velocity, observed in OMNI and Ulysses data. The wind speed is assumed constant beyond 0. 1 au. The temperature is assumed to decay as r À0.
Event based comparison Space weather models can also be evaluated with other types of metrics. Owens (2018)  discussed the various advantages and disadvantages of the method described before with regard to event-based methods. Event-based comparisons focus on the ability of a given model to predict a number of events characterized by some properties. For instance, as we are interested in HSSs, we can define an HSS event as a wind velocity increase above some threshold. This technique attempts to match predicted and observed events and then assess the performance of the models. We define an HSS event in the OMNI data as a continuous period of more than 12 h, where the wind speed is above 400 km/s from January 1st to June 30th of 2018. Following  Reiss et al. (2016) , we then construct a list of events for the observed data and each of the HelioCast computed models.
Acknowledgements. The IRAP team acknowledges support from the  French  space agency ( Centre National des Études Spatiales (CNES , https://cnes. fr/fr) that funds the  Centre de Données de la Physique des Plasmas  (CDPP, http://cdpp. eu) and the Solar-Terrestrial Observations and Modelling Service (STORMS, http://storms-service. irap. omp. eu/). The work of VR, NP, and APR was funded by the  ERC SLOW_SOURCE  project ( SLOW_SOURCE-DLV-819189 ). The work of NF was funded by the  STFC  grant  ST/W001071/1 ,  P91826. The editor thanks  Ward Manchester  and an anonymous reviewer for their assistance in evaluating this paper.
The convergence threshold on total energy (a. u) and forces (a. u) is 10 -5 and 10 -4 , respectively, for optimizing the structures. Spin ordering is explicitly defined for FM, c-AFM, s-AFM1, s-AFM2 and s-AFM3 magnetic configurations, where s-AFM magnetic unit cells are √ 2 × √ 2 × 1 that of the conventional chemical unit cell. The plane wave cut-off energy (E cut ) for Kohn-Sham valence states was taken as 60 Ry for all the four compounds after performing a rigorous convergence test. We employed the WANNIER90 package  [40]  implemented in Quantum Espresso suite to simulate the low-energy tight-binding model.
A more specific illustration of the databases employed during the research phase can be observed in Figure  3. Indeed, almost 48% of the articles have been selected from Google Scholar, while 18% and 15% of the analyzed papers have been sorted from -respectively -Econpapers and Scopus. Additionally, even though lower in terms of the number of selected articles, the databases Jstor, Science Direct, Springer and Taylor & Francis have been similarly important for the first phases of our research. Concerning keywords, the terms considered included "immigration", "innovation", "patenting", "high-skilled migrants", "economic growth, "productivity", "intellectual property rights" (IPRs), "innovativeness", "innovation propensity" and "innovation networks". To this respect, five specific keywords were recurrent in the considered studies: "immigration", "innovation diffusion", "high skilled migration", "cultural diversity" and "patents".
Out of all the articles selected, 95 have been analyzed, while 12 have been discarded based on the inconsistency of the abstract with the scope and purpose of our research question. In addition, 8 articles have been further added to our selection by virtue of manual search, with a view to supplement and expand the aggregate of data we collected through the evaluation of keywords. Precisely, these concerned studies on the link between immigration and innovation conducted at regional level and, specifically, in the European Union. 11 A total of 115 articles was thus included in our database of considered studies and 103 of those were finally examined during the drafting phase of the article. Notably, 86% of the selected articles followed an empirical approach, while merely 12,6 % tackled the issue from a theoretical perspective.
In the choice of existing literature, attention has also been paid to the recurrence of JEL codes. Specifically, among the selected articles by specific terms and keywords, we focused on the frequency with which the following JEL codes appeared: F22 (international migration), J6 (mobility, unemployment, vacancies and immigrant workers. Peculiar consideration has been dedicated to the JEL Code J61, concerning geographic labour mobility and immigrant workers), O31 (innovation and invention: processes and incentives). 4  is a sizeable recurrence of the Jel code J61, which appears in 29 over 89 selected articles amounting to a relative frequency of 32,6%. Significantly, Jel codes F22 and O31 return in 21,3% and 19,1% respectively of the studies included in our database. The articles'year of publication represent a further selection criterion according to which a personal database for answering the determined research question was created.
(2011)  on European data regarding patent applications, labor force, immigration and production for 12 countries from the General and Regional Database of Eurostat (2011). They refer to 170 regions at NUTS 2 level in the period 1991-2008, and they use an IV procedure to solve endogeneity issues. Indeed, higher innovation could attract a higher number of immigrants, thus boosting the intensity of innovation activities. In order to address this issue,  Ozgen et al. (2011)  instrument immigration with the presence of McDonald's restaurants as a symbol of openness and internationalization of the area. In addition, they use as a dummy variable the presence in the NUTS region of a capital city, which normally represents the entrance spot for many immigrants. It is proven that diversity has a positive and significant impact on patent applications.
7% of the entire degrees awarded. 37 For example, as far as Mexico, El Salvador and Guatemala are concerned, these rank among the least educated immigrants' source countries. Conversely, Filipinos and Indians, which represent respectively the second and third ethnicity by number of immigrants to the United States, are estimated to be significantly better-educated relative to natives  (Card, 2009). 38 For an in-depth analysis on this subject, the authors refer to the work of Lucia  Brajkovic and Robin Matross Helms (2018). Source: National Science Foundation, WebCASPAR database, http://webcaspar. nsf. gov To a great extent, the hiring of foreign students by US universities has been fostered by recent modification of the H-1B visa policy 39 , which has been mentioned in the previous section.
In fact, it is one of the reasons why quantification pervaded the humanitarian field in the first place, notably through the 'need-system'. Yet, the hope associated with the use of quantitative data has clearly grown these last few years, and a more innovative and extensive EBH has been encouraged. The expansion of EBH relies on two main elements. First, as mentioned by Mark Lowcock, technological advances are crucial to developing EBH. The Centre for Humdata is at the forefront of this trend. It makes data more accessible through open platforms, such as the Humanitarian Data Exchange (HDX). It also provides data training to humanitarian workers and develops predictive data analytics. Second, the multiplication of quantitative data sources also plays a huge role. For centuries, the data used for humanitarian actions were mainly first-hand data, that is, data collected by the humanitarian organisations' own means and resources.
However, as second-hand data, that is, data collected by other actors (international organisations, institutes, think tanks) have been made more and more available, they have become new potential materials for humanitarian workers. As part of the DATAWAR research project, this article focuses on a specific object: quantitative data coming from armed conflict databases (ACDs). On the one hand, quantitative data are considered as information that can be counted or measured ('quantified') and given a numerical value. On the other hand, ACDs were defined by the project members as open-source databases that can act as sources of quantitative data on issues directly related to conflicts, including, for example, those documenting the direct consequences of conflict (number of deaths, injuries, violent events), those addressing the environment of conflict (defence expenditures, military capabilities), and those marking the risk of conflict itself (early warning ranking systems, risk indicators).
This particular focus is explained by the fact that many institutes, think tanks and political institutions have, since the beginning of the twenty-first century, taken a greater interest in quantitative studies (De  Franco and Meyer, 2011; Ward et al. , 2013;  Colonomos, 2016; Meyer et al. , 2019). 2  These ACDs are presented as a means to help practitioners make better decisions, a decisive asset for apprehending the international environment. Some have developed specific expertise in conflict-related data, such as the Armed Conflict Location & Event Data Project (ACLED), which focuses on producing data on political violence, and the Military Balance+ of the International Institute for Security Studies (IISS), which offers detailed data on military capabilities. Others have implemented specific tools to allow for a kind of 'objective' comparison between states in the international arena, such as the Global Peace Index, the Fragile States Index, and the Index for Risk Management (INFORM) from the European Union.
Starting with the limited use of ACDs, it expands more broadly on how quantitative data are considered by humanitarian workers. The conclusion qualifies (for the time being) Mr Lowcock's ambitions: EBH may still have a long way to go before becoming embedded in humanitarian actors' practices. In fact, we argue that the situation has not changed much since the introduction of quantitative data within the humanitarian field: quantification mainly serves bottom-up accountability for decision-makers and donors, rather than day-to-day project management. This study discusses three main reasons to explain this limited use of data. First, there is a criticism of the need-system among humanitarian workers, who consider it as a control tool implemented by donors. As a result, they don't want to expand the logic of quantification.
Obviously the Excel spreadsheets, the databases, the cell phones, the emails, and the clouds. But most humanitarian quantification is not 'digital' at all. (2020: 200)   That being said, the trust of 'data optimists' (Fast,  2017) regarding humanitarian practices and quantitative data has not diminished. On the contrary, it continues to grow. Arguments supporting this optimism are addressed by only a handful of humanitarian organisations and international institutions, often major producers and consumers of quantitative data. The recent emergence of dataspecialised non-governmental organisations (NGOs), such as CartONG, ACAPS, MapAction, REACH, IMPACT and iMMAP, has played a key role in the spread of this dynamic through the production of reports and factsheets. This literature, based on the premise that quantitative data is an asset, aims to enhance the development of EBH.
For instance, it provides advice on information management and inventories what could be done with current data and how it could be better managed. Following the objectives of the Centre for Humdata, the ambition is to persuade practitioners that building data skills and strengthening the data literacy of humanitarian workers is necessary. However, it also shows that humanitarian organisations may still have a long way to go to fully embrace the potential associated with quantitative data. The current literature does not allow us to fully grasp the real importance of quantitative data within the humanitarian field. First, a careful investigation of the type of data used by humanitarian workers is clearly missing. Defining a scope of use would make it easier to apprehend the place quantitative data is accorded in daily humanitarian practices.
Thus, we met with project managers, desk officers, humanitarian workers on the ground, and training officers from France and internationally (mainly working in the Horn of Africa). This allowed us to have a better grasp of the character of the issues surrounding the use of quantitative data within the humanitarian field and to appreciate how quantitative data were really integrated in the process of humanitarian work compared to what was shown in annual reports and official discourses. Finally, it was also important to discuss whether open-source ACDs were used in internal processes and if not, to figure out the reasons why. Transcripts were read and analysed by hand. Information on the data used were put in perspective with the analysis of annual reports. Recurring themes were identified and discussed within the team.
The Lasting Influence of the 'Need-System' Table  1  shows how influential the need-system still is in the data consumption of humanitarian workers. All quantitative data mobilised in this sector relate to needs, whether directly or indirectly. Within this framework, there is little room for ACDs. In fact, ACDs were almost never mentioned, even though they could have been used to improve humanitarian workers' daily tasks, such as contextual analysis. For example, the Uppsala Conflict Data Program/Peace Research Institute Oslo (UCDP/PRIO), the Correlates of War Project (COW), the Fragile States Index, the Global Peace Index, or even quantitative data from the Stockholm International Peace Research Institute (SIPRI) (knowing that defence expenditures are still mobilised as an indicator for geopolitical tensions) could be used for contextual analysis.
10  Event databases such as ACLED could be used for contextual analysis or security matters. Early warning systems such as ACLED's Conflict Pulse, the INFORM Risk Index, the PRIO Conflict Prediction or the Violence Early-Warning System (ViEWS) from Uppsala University/PRIO could be mobilised for predictive analysis or for coordination among humanitarian workers. Many of these are mentioned in the Centre for Humdata's 'Catalogue of Predictive Models in the Humanitarian Sector'. 11 However, these potential uses were mostly dismissed by both our interviewees and the content of annual reports. As a result, ACDs are not considered by humanitarian workers because the way they mobilise quantitative data is mainly predetermined by a specific, restrictive, hierarchically oriented evidence-based system.
Yet, humanitarian workers seem quite sceptical of these numbers as well: I was reading a very serious report, a UN publication, in which they said: 'Approximately, we have 973,000 people…'. For me, we can't be 'approximate'. This is a battle that has been going on for years. And I dug into how these people had produced their figures… [rolls his eyes and sighs]. 12  We can also use the UN datasets but if you are in the field, you can have reservations about what it really measures…  13  What is interesting with UN data is that the situation always seems to get worse despite our interventions…  14  Behind these discourses lies a paradox: how to explain humanitarian workers' massive use of quantitative data when they are so critical about it? In fact, it is necessary to understand that the problem does not lie with the quality of data.
One practitioner illustrated it in these terms: According to me, these quantitative databases, this desire to standardize and have indicators, it's important for people at a global level, that is to say important for the donors. They want to know where to invest…. It's not important at all for people at the local level because at the local level you don't think with data. You know what's going on in the conflict, you know who the people are who are affected by it. In fact, the information is already there. 18  In this case, it may not seem relevant to use secondhand data, let alone one ACDs: 'the war to get data is also a war to gain access to funding. This is not open source'. 19  This is different for UN datasets, since they 'define the funding priorities. As we are completely dependent on UN money, if there are things that are not identified as priorities, it can be a problem.
For instance, one interviewee explained: There is a fantasy in humanitarianism that quantification will allow us to ignore the inherent uncertainty we face. Thanks to algorithms, we will be able to relieve ourselves of the difficulty of interpreting ambiguous events about which we have incomplete and unreliable information … and we will forget the fact that the very essence of decision-making in humanitarian situations is to make bets in the face of uncertainty. 34  Thus, part of the resistance to databases and quantitative data lies in the desire to keep the decision-making process as human as possible, based on staff expertise. Current EBH is seen as promoting the opposite: So these systems that want to do a lot of data in a short amount of time forget that the value of humanitarian information is linked to the trust that exists between the person who gives the information and the person who collects it.