dataesr/scientific-paragraphs-categorization huggingface dataset:
To meet these aims, a methodology focusing on the users' perception of the tool was developed during Phase 1 of the CoEvalAMR network  (11). The original methodology was used by Sandberg et al. (11)  to provide feedback on six different evaluation tools based on their application in eight countries. Based on the experience gained, the methodology has recently been updated and further refined, as part of the work undertaken in Phase 2 of the CoEvalAMR network  (12). The methodology encompasses the evaluation of descriptive and functional aspects, together with an assessment of content themes and questions on strengths, weaknesses, opportunities, and threats (SWOT)  (12). OH-EpiCap is among the catalog of tools being assessed in Phase 2 of the CoEvalAMR network. This tool has recently been developed by the MATRIX consortium, funded by the One Health European Joint Program to systematize the characterization of epidemiological surveillance activities in a national surveillance system (13).
Data collection The nine surveillance systems evaluated were selected by members of the CoEvalAMR network. The selection was made by convenience The number of respondents involved in the evaluation of each case study varied from one to five; these respondents are referred to as "assessors" throughout the text. The assessors filled in the OH-EpiCap evaluation questions during either a single or repeated workshop session that lasted a total of 2-8 h. All assessors involved had expertise in AMR surveillance in the country they represented for this study, scoring the indicator questions according to their own experience or knowledge from previous activities. This methodology makes the evaluation outputs somewhat subjective. In the country case studies that were conducted by more than one assessor, the subjectivity was reduced because of the requirement to reach consensus within the group of assessors who formed part of the country case.
An evaluation using OH-EpiCap can be conducted in a short period of time and with a small group of stakeholders, making it feasible to conduct an evaluation in situations with low resources. Moreover, evaluations can be done recurrently, when changes are implemented, benchmarking the system with itself over time. This can be made easily as OH-EpiCap contains benchmarking functionalities. These functionalities were not investigated in the present study, because of the different aims and purposes of the systems evaluated as noted in Table  2. For example, the Danish DANMAP program serves the purpose of integrated monitoring of AMU and AMR for both the animal and human sectors. In contrast, the Italian ClassyFarm encompasses mainly farm-level risk categorization components (e. g. , biosecurity and animal welfare, besides AMR and AMU) whereas AMR surveillance in the human sector is conducted by different Italian institutions  (19).
We found that most of the indicator questions were considered simple and straight-forward. However, considering the expected worldwide application of the tool by users, who may have different use of the English language and, hence, familiarity with the terminology used, materials should be developed to unequivocally clarify the meaning of all indicators. With the publication of case studies evaluations and the scientific paper accompanying the tool (13), this should be accounted for. At the time of writing, the OH-EpiCap tool was still in a Beta Version, so the phrasing of indicators was not final.
Acknowledgments The authors acknowledge  Henok Ayalew Tegegne ,  Carlijn Bogaardt ,  Géraldine Cazeau ,  Renaud Lailler ,  Johana Reinhardt ,  Emma Taylor ,  Joaquin M. Prada , and  Viviane Hénaux  for the development of the OH-EpiCap tool and for making it available to conduct this work, as well as the stakeholders who provided information to conduct the evaluations in the different country cases.
This article describes the construction and content of the Morts pour la France database. This database contains individual-level data on the universe of the 1. 3 million French fatalities of the Great War who were officially recognized as war victims. It provides information on each soldier's first and last names, dates of birth and death, circumstances of death, recruitment status, military rank and unit, and locations of birth and recruitment. We further provide datasets of military death rates at the level of 87 départements and 35 thousand municipalities along with relevant shapefiles and discuss potential applications of this database.
This labor supply shock perpetuated across generations until today  (Gay, 2023). Politically, the Great War may have set the seeds of France's rapid defeat to Germany in 1940  (Bloch, 1946)  and some its features help explain collaboration with Nazi Germany upon Philippe Pétain's accession to power  (Cagé et al. , 2023). This article describes a novel database that will help improve our understanding of the demographic, economic, and political consequences the Great War on French society: the Morts pour la France (MPF) database (''Dead for France''). The MPF database includes a host of individual-level information for each of the 1. 3 million French individuals who died as a result of the war: their first and last names, dates of birth and death, circumstances of death, recruitment status, military rank and unit at the time of death, and locations of birth and recruitment.
In particular, location information in the MPF database is available at the municipality level, France's most disaggregated administrative unit. The unprecedented granularity of the MPF database opens several avenues for research into the characteristics and consequences of the conflict. In particular, we provide a dataset of military death rates at the level of 35 thousand municipalities, which dramatically improves upon the sources of geographic variation that have been used to identify the consequences of the war. Up to now, researchers had nearly exclusively relied on subsamples of the underlying MPF data aggregated at the level of 87 départements or 21 military regions  (Gilles, 2010; Abramitzky et al. , 2011; Gilles et al. , 2014; Beau, 2017; Guironnet and Parent, 2019; Boehnke and Gay, 2022; Gay, 2023).
1   The MPF database will also further improve our understanding of battle-specific violence, as it contains information on circumstances and dates of death as well as the military ranks of deceased soldiers. For instance, we document that the deadliest day of French history was  September 25, 1915, when 22,059  French soldiers died on the first day of the (ultimately failed) Second Battle of Champagne. Previous historiography instead supported that the deadliest day of the conflict occurred on  August 22, 1914 , in the context of the Battle of the Frontiers  (Steg, 2013)  while other advanced instead the Battle of the Marne in early September 1914 as the deadliest day  (Guillot and Parent, 2018). The precision and exhaustiveness of our database will also improve our understanding of the sociodemographic consequences of the conflict, which so far have mostly been studied based on subsamples (see footnote 1).
As an example, in Section 4, we extend  Guillot and Parent's (2018)  analysis of the determinants of soldiers' survival duration -based on a sample of 18 thousand deceased soldiers -to the full database. Finally, combined information on soldiers' death and rank will enable economic historians to study organizational aspects of the conflict, in the spirit of  Ager et al. (2022) , who show how public recognition induced a shift in the mortality profile of German fighter pilots during World War II, or  Dippel and Ferrara (2023) , who study the effectiveness of military leadership in the context of the US Civil War. There has been comparable efforts to study the implications of the war using individual-level military fatalities information in other European belligerent countries. In the case of Weimar Germany, De Juan et al.
g. ,  Fornasin et al. (2019)  on Italian soldiers,  Kanazawa (2007)  and  Bailey et al. (2023)  on British soldiers, and  McCalman et al. (2019)  on Australian soldiers. More broadly, this article relates to the literature that uses individuallevel military records to study health, wealth, and migration of historical populations  (Costa and Kahn, 2008; Cranfield and Inwood, 2015; Bailey et al. , 2016; Costa et al. , 2018b,a; Cunningham, 2018; Fourie et al. , 2020; Salem, 2023). The largest effort to date in this literature concerns the Union Army during the American Civil War  (Fogel et al. , 2000; Dippel and Heblich, 2021; Weaver, 2022). 3 Similar data collection efforts are now being undertaken for the Confederate Army  (Hall et al. , 2019). In the rest of this article, we first provide some historical context about the organization of the French Army and the archival source of the MPF data (Section 2).
Historical context This section first discusses the organization of the French Army during the war (Section 2. 1) then provides an overview of the construction process of the archival source of the MPF database (Section 2. 2). 4 1 Related studies using subsamples of the same data source as that of the MPF database include  Rolland (2005)  for an analysis of suicides, Le  Gall (2013)  for the geography of recruitment,  Guillot and Parent (2018)  for survival rates,  Moura (2018)  for the characteristics of soldiers who died on battlefields in Meuse and Somme, and  Darlu and Chareille (2020)  for the disappearance of last names. Vandenbroucke (2014)  and  Knowles and Vandenbroucke (2019)  study the implications of the war on fertility though use a calibration approach based on national-level data.
2 In a previous effort,  Koenig (2023)  approximated the constituency-level presence of veterans by the relative growth of the male population to study WWI veterans' support for right-wing parties. 3 Spanning three decades of data collection effort, the Union Army Data  (Fogel et al. , 2000)  consists in a sample of 40 thousand soldiers -a 1. 6 percent random sample of whites mustered into the Union Army -together with additional samples of six thousand Black soldiers, White recruits in six major cities, and prisoners of war who survived until 1900. This database has recently been expanded to the 2. 2 million volunteers of the Union Army by  Dippel and Heblich (2021)  and  Weaver (2022). Dippel and Heblich (2021)  rely on the Union Army Registers reports issued by each US State's Adjutant General's Reports.
These reports includes soldiers' full name, enlistment and discharge date, age, military unit, rank, promotion, whether they volunteered, were drafted or commissioned, their State of enlistment, and their status at the end of the war. Weaver (2022)  uses an alternative dataset, the American Civil War Research Database. 4 Appendix Table  B. 1 provides the list of abbreviations used in this section. The list of laws discussed therein and links to their original texts are provided in Appendix Table  B. 2. In particular, to support our assertions, we refer to the relevant legislation, which is generally available in the Journal Officiel de la République, Lois et Décrets, abbreviated JOLD.
The Mort pour la France records Mort pour la France individual records constitute the underlying source of the MPF database. This archival material was the outcome of a complex process that involved the Mort pour la France recognition and the preparation of the Livre d'Or memorial through most of the 1920s. The Mort pour la France recognition. As early as Winter 1914, the army was facing unprecedented mass mortality  (Prost, 2008; Bach, 2010). To foster patriotism and mitigate the risk of demoralization, the government implemented a set of measures to pay tribute to those who had died in its name  (Petit, 2016). The idea of a honorary title attributed to fallen soldiers first materialized in December 1914, eventually passing into law in July 1915  (JOLD, 47(184) , 4653-4). 12  The Mort pour la France honorary title represented an official recognition that was awarded to all military personnel of the French Army as well as to the French civilians who had died as a result of military operations between August 2, 1914, and October 24,  1919.
1. Scanned records made available through the MdH web platform were initially indexed by the BAVCC through five fields: first name, last name, date of birth, département of birth, and country of birth. To gather information on the remaining fields -representing close to 20 million cells -starting in November 2013, the MdH web platform made it possible to annotate Morts pour la France records online  (Aufray, 2017). Ten days later, the initiative ''1 Jour-1 Poilu'' was launched, mobilizing ''citizen historians'' through social media in order to fulfill this immense task  (Gilot et al. , 2018). 23 Thanks to the participation of 25 thousand contributors, the indexation of all the fields of the Morts pour la France database was achieved on April 30, 2018  (Gilot, 2020). On January 22, 2019, the resulting annotated database was first made downloadable in csv format on the MdH web platform.
A database of French fatalities of the Great War In this section, we provide an overview of the variables in the MPF database: soldier's first and last name, dates of birth and death, circumstances of death, recruitment class, military rank, military unit, location of birth, and recruitment bureau-see Tables  1  and 2  for a summary of the content of the MPF data files. Raw data files available on the MdH web platform as of September 2023 contain 1,825,593 observations corresponding to 1,422,003 unique MPF records. Starting from these files, we drop duplicate records and soldiers born outside of mainland France. 25 25 There are close to 400 thousand duplicate records in the raw data files because one additional observation is created whenever a given field on an MPF record contains multiple annotations.
Circumstances of death. While a field exists to record the circumstances of death in the original MPF records and is filled in most cases with the mention ''killed in action'' (tué à l'ennemi), this field was (the only one) not indexed by the BAVCC on the MdH web platform. However, the field that indicates the location of death sometimes includes information that we can use to infer soldiers' circumstances of death. This field contains heterogeneous information, from the actual location of the death, such as a given battlefield or hospital, to the circumstances of the death, such as a disease or an accident. In nearly half of cases, however, this field is empty in the MdH data files. In these cases, we assume that the soldier was killed in action. From there, we create two variables: one that provides the type of death across 12 categories and one that qualifies this typology, indicating when the circumstance of death was missing in the MdH data file so as to warn users to exert caution when using this variable.
9 percent for the 1895 cohort and 0. 03 percent for the 1899 cohort (Appendix Figure A. 9). Information for recruitment class is available for 98. 53 percent of MPF records. 32 For 14,093 records, the original recruitment class is more than three years apart from the soldier's birth year plus 20, which is likely erroneous, and for 18,208 records, the recruitment class is missing (1. 43 percent of MPF records). In these cases, we impute the recruitment class with the theoretical value of birth year plus 20. Appendix Table  B. 11 provides a breakdown of these cases, which are flagged in the MPF database. Military rank (at time of death). We normalize MPFs' military ranks at time of death into 51 categories so that each category contains at least 10 observations-we classify residual categories into an OTHER category, which contains 53 records.
Military unit (at time of death). We normalize MPFs' military units at time of death into 250 entities further divided into 16 categories (Appendix Table  B. 13). The overwhelming majority of MPFs were part of the infantry, with close to 1. 1 million records (86. 08 percent of MPF records), and generally part of an infantry regiment (64. 90 percent). 33 Other units include artillery (6. 59 percent), engineering (2. 23 percent), cavalry (1. 62 percent), and miscellaneous categories (3. 48 percent). 34 Despite being born in metropolitan France, nearly 10 percent of MPFs belonged to a colonial military unit (see Footnote 2. 1 and the example in Fig. 1 ). Again, the data coverage for this variable is substantial as only 7,223 records (0. 57 percent of MPF records) do not contain information on military unit, not counting civilians.
01 percent of MPF records for which the military unit information is available. 34 Reflecting the evolution of the strategic importance of the infantry relative to the artillery over the course of the war, the share of infantry among MPFs declined from 94 percent in 1914 to  74 percent in 1918 and 54 percent in 1919 (Appendix Figure A. 10). 35 Cassini identifiers are from the Histoire Administrative des Communes (HAC) database, initially distributed through http://cassini. ehess. fr, now available as a csv file on EHESS' Didoména repository  (Cristofoli et al. , 2021). 36 Appendix Table  B. 14 provides the list of the 40 most frequent municipalities of birth along with their frequencies and Appendix Table  B. 15 provides the distribution of unique municipalities. While Paris appears as a single municipality in these tables, MPF information is broken down across Paris' 20 municipal arrondissements in the database.
Applications This section provides three examples of how the MPF database might be used for empirical analysis: first, by aggregating MPF records over space to create military death rates and approximate the demographic shock of the war at different geographic levels (Section 4. 1), second, by aggregating MPF records over time to shed light on the most deadly battles of the war (Section 4. 2), and third, to leverage the individual-level data for an analysis of survival duration during the conflict (Section 4. 3).
Aggregate-level military death rates An important application of the MPF database is the aggregation of MPF records at various geographic levels to measure the demographic shock of the war. Indeed, the sex ratio among adults aged 15-50 dropped from 98 men per 100 women at the onset of the war to 88 by the end of the war, and it was not until the early 1950s that it reverted back to balance (Appendix Figure A. 12). 39  This type of analysis has been done by previous research both at the département  (Gilles, 2010; Gilles et al. , 2014; Beau, 2017; Guironnet and Parent, 2019; Boehnke and Gay, 2022; Gay, 2023)  and at the municipality levels  (Cagé et al. , 2023). We discuss these applications below and provide some insight on how to properly use these military death rates variables.
Département-level military death rates We first create a dataset of département-level military death rates by calculating the ratio of MPFs to the size of a département's drafted population during the war, which we approximate by the number of men aged 15-44 in the census of 1911, the last census before the war. 40 We display the distribution of military death rates across départements in Fig. 3. They range from 6. 7 percent in Alpes-Maritimes to 29. 3 percent in Lozère, with an average of 16. 1 percent and standard deviation of 3. 8 percent (Panel A of Table  3 ). 41  As highlighted in Section 2. 1, the systematic part of the distribution of military fatalities was determined by the territorial organization of military recruitment and the industrial war effort. Soldiers recruited in the same military region were initially grouped into the same units and sent to the same battlefields.
Municipality-level military death rates To analyze the implications of the war at a more disaggregated level, we create a dataset of municipality-level military death rates based on our MPF database. Municipalities are the most disaggregated administrative level in France, as the country counts about 35 thousand municipalities. Unfortunately, the census of 1911 does not provide municipality-level population by age and gender, so we are constrained to rely on total population in the denominator. For consistency, the numerator therefore further includes civilian and female MPFs who were born in mainland France outside of Alsace-Lorraine and died between August 2, 1914, and October 24, 1919. We display the resulting distribution of military death rates in Fig. 4. 44 Among the 34,893 municipalities of France, only 92 had no MPF-99.
44 Note that we are constrained to map this variable in 2015 geography because municipality-level shapefiles for 1914 do not exist to date. For consistency in the matching process between the pre-war geography and that of 2015, we correct the 1911 population data for municipalities created after the war by attributing the population of their parent municipality in proportion to the population ratio between them and their parent municipality at the time of their creation. We proceed in the same way for the MPF data except that we correct figures for municipalities created after 1891. 45 Municipality-level death rates were previously made available by  Cagé et al. (2023)  using an early version of the MPF database. However, they did not correct for changes in municipalities' geographies, so that we advise users to rely on this updated database of municipality-level death rates.
The deadliest days of the war A second potential application of the MPF database is in the study of the dynamics of the conflict. For example, Fig. 5  plots the time series of aggregated daily dates of death in order to shed light on the most deadly battles of the war. It reveals that the deadliest day of the conflict was the first day of the Second Battle of Champagne on September 25, 1915, an ultimately failed offensive for the French Army. On that day, 22,059 soldiers died. Many historians assert instead that the date of August 22, 1914, during the Battle of the Frontiers, was the deadliest day of the conflict (see, e. g. ,  Steg 2013). Our data record this day as the second deadliest day of the conflict with 19,964 fatalities. 46  Overall, the Battle of the Frontiers of 1914 remains a period of very high mortality.
An analysis of survival inequalities on the front A third potential application of the MPF database is in the study of the determinants of soldiers' survival rates during the war, an analysis previously done by  Guillot and Parent (2018)  on a non-random sample of 18 thousand deceased soldiers -a 1/52nd sample of the MPF database -born during the first seven days of the month of October, regardless of the year. In this section, we reproduce their analysis based on the full MPF database. We focus on non-civilian male MPFs of the cohorts 1866-94 born in mainland France outside of Alsace-Lorraine who died between  August 2, 1914 and November 11, 1918 , for whom the death date is available. 47 We define survival duration (in days) as the difference between the date of death and 2 August 1914, the first day of the war-conditional on dying during the war, soldiers survived on average for 528 days.
We then estimate through linear regressions the relationship between survival duration and the age of soldiers as of August 2, 1914 (in days), their military unit (where the excluded category is Infantry), their military rank in the colonies. The database also counts men who died of diseases on a given day, but these men may have been injured on another day. It therefore remains to be shown whether these differences would make up for the difference in fatalities between what is commonly accepted as the deadliest day of WWI for the French Army and what the MPF database reveals. Another possibility is that some of the previous quantitative studies of the conflict, e. g. ,  Guillot and Parent (2018) , relied on the 1866-94 cohorts that were already drafted at the start of the conflict, excluding the new cohorts that were incorporated as the conflict dragged on.