dataesr/scientific-paragraphs-categorization huggingface dataset:
These disagreements lead to practical problems. For example, CHIELD depends on agreement in variable names. Small differences in labelling lead to major changes in the network. Very general node labels such as "language" are also unhelpful when trying to identify causal paths between theories. These issues are mitigated to some extent by the provision of term recommendations on data entry and the ability for anyone to edit causal graphs after initial submission. Fully unifying the vocabulary would take a lot of editing work, but this might be worth the effort in order to improve research communication in the field. The final challenge was that a theory cannot be fully understood from its graph alone. For this reason, it is strongly encouraged to add quotes from the paper in the "notes" field that include important context.
However, combined with some general background knowledge of the field, causal graphs can provide a very helpful overview of ongoing and existing research. Indeed, we found that the process of coding causal graphs increased the coder's own clarity of the paper beyond simply reading the text. We maintain that CHIELD is a useful tool for researchers to organise their research, but we are aware that it would take a huge amount of work to produce a definitive overview of the whole field, if this were at all possible. The challenges above affect the scalability of the database as the number of contributors and topics expand. We are currently optimistic. Thanks to the use of git and Github, the current system seems adequate for handling the amount of data and edits (around 1000 edits to causal graphs).
CHIELD serves a relatively large and diverse set of researchers (41 contributors from multiple disciplines e. g. anthropology, cognitive science, computational modelling, genetics, morphology, philosophy, primatology, psychology, sociolinguistics, syntax, and typology), who have been able to contribute despite many having little technical or programming backgrounds. Although anyone can suggest edits, there is currently only one moderator, and the project may need to expand to one or more committees for dealing with sub-topics or variable names. We suggest that expansion to other fields may be best done by starting a seperate database using the tools and structures of CHIELD as a template (e. g. the Hypothesis Database for Research into the Evolution of Culture, HyDREC https://github. com/jwinters/HyDREC), rather than trying to fit too many fields in a single source.
Refining the database may be facilitated by dedicated sessions at workshops. One major bottleneck is training in causal inference methods for the coders. We hope that this can become a more central part of research training in general in the future. More generally, the data in CHIELD is designed to be used with the Directed Acyclic Graph (DAG) approach to causality and the tools for dealing with them. However, there are limitations on the kinds of causal relations that can be represented in this way, and other approaches are available (see e. g. Mahoney, 2008; Granger, 2016 , Blasi & Roberts, 2017). In particular, causal loops can be graphically represented using DAGs, but they violate some of the assumptions that allow various parts of the causal inference machinery to function. Similarly, inference is complicated by non-monotonic effects  (VanderWeele & Robins, 2010)  or effects happening on different timescales  (Aarlen et al.
2016). Many of the theories coded in CHIELD include these features, which limits quantitative treatment and complicates searching for ancestors and links between theories. However, we believe that the current approach still has qualitative value and as methods and theory develop, CHIELD and the data therein can grow to alleviate these shortcomings. We note that there are existing attempts for automatic extraction of causal relations directly from publication texts (see  Alshuwaier, Areshey, & Poon, 2017; Asghar, 2016; Mueller & Abdullaev, 2019; Mueller & Huettemann, 2018; Tshitoyan et al. , 2019). However, given the problems above, we are sceptical of the effectiveness of this approach for evolutionary linguistics. In any case, progress in automated coding of causal structure would require human-coded, "gold standard" data, which could be provided by CHIELD.
We presented the design and implementation of CHIELD, a database of causal hypotheses in evolutionary linguistics. We demonstrated CHIELD's uses, including identifying critical differences between theories, discovering critical evidence, synthesising theories and finding collaborators. The main challenge is in the coding of hypotheses. However, the challenge derives mainly from the difficulty of accurately conveying causal ideas in prose, rather than any limitation of causal inference tools. This issue would be assisted by the use of causal graphs to express hypotheses in the first place. One possible solution would be for journals to encourage that authors submit a causal graph with each publication (as a figure or as metadata). This could then be more easily fed back into CHIELD order to strengthen the representation of the database.
There are many ways that CHIELD could be extended in the future. For the database itself, we plan to add permanent links to the variables and documents, add statistical results to edges (though there is the question of how to allow multiple types of statistic to one edge), and add more flexibility in searching (e. g. , limit the results to a particular stage of language evolution). There is also the possibility of converting to database formats that explicitly support network structures for advanced querying (e. g. , GraphML). We would like to add support for large touchscreens and interactive debate so that CHIELD could be used as a tool for live discussion between researchers. As research methods increasingly involve collaboration with multiple researchers, this could help people check how their understanding of terms and concepts match.
We would also like to develop a way for CHIELD to automatically suggest studies (e. g. , for student projects). For example, randomly choosing a causal link that currently has no empirical evidence, detecting colliders that might require alternative explanations, or showing a link between two theories that might not have been noticed. It would also be possible to link nodes to open source data, so that CHIELD could try to discover links that currently have no empirical support, but for which there was existing data that could be utilised. Of course, automated procedures may be susceptible to bias (e. g. , publication biases), and they can be no replacement for careful research practice. But we hope CHIELD can help make research more systematic. In particular, we are keen to explore how the data in CHIELD can be used as prior biases in more automated machine learning processes, such as automatic causal graph inference.
In conclusion, clearly expressing complex scientific hypotheses is hard. There is a need for formal tools, such as causal graphs, to help researchers communicate their ideas. CHIELD provides easier access to these tools and creates a space for clarifying theories. It demonstrates that the field of evolutionary linguistics is more connected than we thought, and there are potentially many links between theories that are waiting to be discovered. We suspect the same is true for many other fields that would also benefit from building their own database of causal graphs.
Acknowledgements AD, CS, FJ and ETC were funded from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 639291 , Starting Grant VARIKIN). JN was supported by a  Principal's Career Development Scholarship  from the  School of Philosophy, Psychology & Language Sciences at Edinburgh. KS was supported by an  Academy of Finland  grant ( 296212 ) and by an  ERC   starting grant  ( 805371 ). RS was supported by the  Australian Research Council  ( FL130100111 ). SGR is supported by a  Leverhulme early career fellowship  ( ECF-2016-435 ). SFM is supported by an  Australian   Government Research Training Program  (RTP) Scholarship and  Australian Research Council   Laureate Fellowship Grant   FL130100141. CS was supported by the  John Templeton Fund (40128).
The authors thank the referee for a careful reading of the manuscript and A. McWilliam for useful discussion of abundance error propagation. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Su- Facilities: Magellan/Clay MIKE Software: MOOG  (Sneden 1973; Sobeck et al. 2011) , IRAF  (Tody 1986 (Tody , 1993)) , ATLAS9  (Castelli & Kurucz 2003) ,linemake(https://github. com/vmplacco/linemake), STARFIT (http://starfit. org/).
Methods: We used a participatory approach to map all the paths, residential areas, buildings and rice fields on Open-StreetMap (OSM). We estimated shortest routes from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. Then, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. Travel speed models were calibrated with field data obtained by GPS tracking in a sample of 168 walking routes. Model results were used to predict travel time to seek care at PHCs and CHSs for all the shortest routes estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny.
Results: We mapped over 100,000 buildings, 23,000 km of footpaths, and 4925 residential areas throughout Ifanadiana district; these data are freely available on OSM. We found that over three quarters of the population lived more than one hour away from a PHC, and 10-15% lived more than 1 h away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 h away, and vulnerable populations across the district with poor geographical access (> 1 h) to both PHCs and CHSs.
OSM mapping of Ifanadiana district was achieved in 8 months with the collaboration of 103 participants. To increase participation in the mapping project, we organized 5 "mapping parties" with local universities and OSM groups in both Madagascar and La Reunion. Despite it being a collaborative project and published in the HOTOSM Task Manager, we had few spontaneous contributions and 5 people from our research team mapped 73. 6% of the overall project. The geographic data mapped in OSM is now freely accessible to any user and can be queried on QGIS  [55]  via the QuickOSM plugin, which we used here for retrieving the data for our analyses.
Recording travel time on the field Most travel in Ifanadiana district is done by foot due the minimal transportation infrastructure and steep terrain. To obtain context-specific estimates of travel speed by foot according to terrain characteristics, we recorded GPS data from 168 walking routes across 10 out of the 15 communes of Ifanadiana district between September 2018 and April 2019 (Additional file 1). We collected two types of routes: 1) routes from field expeditions of PIVOT's community team staff during CHW supervisions, and 2) routes specifically recorded for this project to obtain a larger sample size and wider representation of terrain characteristics, collected by representatives of the PIVOT research team and by the local population. We recorded these tracks using Samsung Tab A10 tablets and the Android app "OsmAnd" version 3.
We finally computed a confusion matrix to compare observed and classified values by class. In addition to land cover, we obtained elevation and precipitation data from remotely sensed data. We downloaded the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) from the United States Geological Survey (USGS,  [59] ), which gives elevation with a 30 m ground resolution. We also acquired precipitation estimates from the NASA Prediction of Worldwide Energy Resources (POWER) Project  [60] , with a spatial resolution of 0. 5 * 0. 5 degrees.
Estimation of shortest path distance Once the mapping of all buildings and footpaths was finalized, we used the OSRM software  [61]  to calculate the distance of the shortest path between each building and the closest health facility, via the R software package "osrm" (Fig. 1 ). OSRM uses the Dijkstra's routing Algorithm, which searches iteratively the shortest path from a single node to the destination node in a network. For each building, the shortest path distance to two health facilities were calculated: to the closest PHC and to the closest CHS. While we had the location of all PHCs and the 37 CHSs built with PIVOT support, for CHSs without precise GPS locations (158 out of 195), we assumed that they were located in the main village of the Fokontany ("chef lieu"), as indicated in national policies for community health.
Estimation of travel speed and time to seek treatment To obtain precise and context-specific estimates of time to seek treatment, we studied the geographic and climatic factors associated with travel speed in the sample of 168 walking routes collected on the field. For this, travel speed between each pair of points within a GPS track was estimated using the time and GPS location at which each point was taken. Then, we divided the 168 walking routes into 100 m segments and we intersected these segments with the raster datasets to obtain corresponding values for DEM, land cover and rainfall. For this, we used the "st_intersects" function available in PostGIS software. As a result, between each pair of points (N = 57,719) we obtained values for the following explanatory variables: degree of slope, cumulative distance since the beginning of the track, precipitation and land use.
Since rainfall affects travel speed and varies from day to day, for each route we provided a prediction for a scenario without rainfall (minimum time) and with the maximum amount of rainfall recorded during fieldwork (maximum time). Although predictions of travel time for these routes were not validated a posteriori on the ground, the 168 fieldwork routes used to parameterize the model were representative of the larger PHC and CHS route datasets (Additional file 1). As with travel distance, we used kriging methods to interpolate the values of travel for the entire district to improve visualization.
Comparison of results with existing methods To assess whether the methods used in our study improved the precision of existing methods used in geographic accessibility modeling, we compared estimates of travel time obtained here with results obtained from Euclidean and friction surface methods. For this, we compared absolute and relative differences for the 168 routes for which we had field information as well as for the 41,426 routes predicted in our analyses. For Euclidean distance we used the R package "stats" and we assumed a constant travel speed of 5 km/h. For friction surfaces, we used the software "AccesMod" together with the district's digital elevation model, OSM road network and land cover datasets, and we assigned speed values for each class of land cover and road network following recommendations for AccessMod 3.
Mapping A total of 106,171 buildings were mapped. Of these, 65. 6% were located in one of the 4925 residential areas, whereas 34. 4% were isolated houses. The size of residential areas ranged from 4 to 870 buildings, with an average size of 14. Moreover, 23,726 km of footpaths were mapped, which represent 99. 1% of the road network in Ifanadiana district. Only 0. 3% of the road network (62 km) are paved secondary roads. To expand available data, we also mapped on OSM the name and location of 707 villages, 21 PHCs (2 of which were recently built), and 37 CHSs built with PIVOT support. This district GIS dataset was substantially more exhaustive than what is available through OSM for the rest of districts in the Vatovavy Fitovinany region, comprising an area of approximately 16,788 sq km (Table  1 ).
e-Health tools for geographical accessibility to care The web interface created for health workers and program managers (Fig. 5 ) allows visualization of key information for the planning and implementation of health programs such as (1) areas with the lowest accessibility to health care based on shortest path distance and travel time, (2) the percentage of the population in each commune and fokontany that are at a particular distance and travel time to the nearest health facility, (3) a tool to estimate shortest path routes for field expeditions and community health work with either a satellite or OSM background, and 4) the geographic distribution of residential areas and isolated households that are within a certain distance or time from a selected health facility. In addition to the web interface, which can be accessed by phone or computer but requires an internet connection, field workers can get accurate directions without internet access.
Given that most travel in rural areas is done by foot, inaccuracies from such models can be quite important  [22, 71]. In addition, while travel time can strongly depend on contextual factors, researchers use either constant values for travel speed  [20, 26]  or values for different geographic features inputted from other contexts  [22]. In our context, comparisons of our estimates with these methods showed average differences in travel time of 30-35% for Euclidian distances and 20-25% for friction surfaces. Our approach could be replicated in future studies of geographical accessibility to care in rural areas of the developing world, where very precise estimates of distance and time to seek treatment are required for local use by decision-makers, program managers and health workers. Mapping on OSM is simple, the information entered is accessible to anyone for download or for use into mobile apps, and the geographic database can be regularly updated or further expanded by the online community.
Regarding climate, our estimates under scenarios with and without rainfall differed only by up to 20 min at distances of over 15 km. Although the district has a tropical climate with periods of heavy rains, track GPS recordings of field expeditions were collected during relatively low rainfall periods (up to 47. 5 mm per day), which could explain the small impact of rainfall on travel speed. In addition, the coarse resolution of available rainfall data for our area (0. 5 degrees) could have biased the estimates of rainfall and impacted its influence in our model. Rainfall could also lead to the isolation of areas during the rainy season due to flooding or a rise in river water levels  [35] , but we could not estimate these effects in our setting from remote sensing data. In terms of land cover, only water surfaces and rice fields were associated with lower speed values, and removing land cover effects resulted in minimal changes in our predictions of travel time (Additional file 9).
, 2014). The delay for detecting and delegating the recorded triggers to external equipment is an important factor due to the temporal nature of the recorded neurological signals  (Plant et al. , 2004). Inaccurate stimulus timing can cause both Type I (false positives) and Type II errors (false negatives). As a solution to the problem of detecting analog and digital events and delivering them to different systems without significant delay, we describe an inexpensive and ultra-low latency device that we designed and built to detect and deliver visual and auditory stimulus onsets to multiple external data acquisition systems. The code and design of the system are further described at the project's website (https://stimulusonsethub. github. io/StimulusOnsetHub/). Our goal was to design and build a robust and affordable device that would perform its duties with minimal delay while minimizing electrical crosstalk between the different externally connected systems.
(see Figure  2 )  (Plant et al. , 2002). We requested quotes for stimulus trackers from three different companies and received prices ranging from 1,645 to 4,488 US dollars. Each company offered add-ons to interface with more than one external recording device, but these were also rather expensive. For example, one company offered an adapter for our BioSemi Active Two (BioSemi, Amsterdam, Netherlands) data acquisition system for 125 US dollars along with another adapter for our Eyelink 1,000+ eye tracker (SR Research, Ottawa, ON, Canada) for 55 US dollars. Another company charged 395 US dollars for all three connections that we needed. No company seemed to offer opto-isolation, but we couldn't be sure without opening each device. Instead of purchasing a commercial system with unknown schematics, we spent about 200 US dollars and made the opto-isolated device ourselves.
Code Design We programmed the hardware in the Arduino C Language and made it freely available on the Internet and named it StimulusOnsetHub  (Martin and Davis, 2019). On the GitHUB page (https://stimulusonsethub. github. io/ StimulusOnsetHub/), there is also a hardware list and more detailed information for how to build the device along with suggested tools, computer hardware, and other components for building highly accurate behavioral experiments. In the code, the "setup()" function is called once when the Arduino powers up, and sets up all pin modes and interrupts for the different connections. The "loop()" function is called repeatedly while the box is running and handles: • Reading the photodiode and microphone input levels, • Sending digital TTL output signals if the analog/digital input levels crossed the user-specified threshold level for each input, and • Optionally updating the LCD if an interrupt from one of the rotary encoders was triggered.
• DIGITALWRITEFAST: Optionally use the digitalWriteFast library  (Watterott, 2017). • DIGITALMICROPHONE Optionally use a digital microphone. • ADDA: Optionally support an analog daughterboard. • FASTADC: Optionally set different pre-scale methods on potentially connected analog to digital converters. • DEBUG: Optionally enable debug statements to be sent to the console. This compiler option is for debugging and verifying the software and hardware. We encountered a limitation with the Arduino in that we could only use one of the analog inputs at a time because of preexisting time constraints on switching between the reading of different analog channels. The Arduino could only check the analog input rail one analog input at a time. For example, if a photodiode was hooked up to analog input A0 and an analog microphone was hooked up to analog input A1, the Arduino could not read the signal from A0 and the signal from A1 at the exact same time.
RESULTS We measured the processing delay through the box using the digital inputs. To perform this test, we used the oscilloscope to average 30 iterations of a pure-tone sound wave that was played through the speaker. The digital output of the microphone was then used as the input to both the stimulus onset hub and oscilloscope. As verified with the oscilloscope, the digital latency through the box had a maximum input/output delay of only 4 µs (Figure  8A ). To test the effects of different thresholds on the temporal precision of the TTL output signals, we wrote a benchmarking tool in Matlab using Psychtoolbox  (Brainard, 1997). The Psychtoolbox program alternately painted 500ms black and white squares on an ASUS PG278Q computer screen with a refresh rate of 120 Hz to trigger the photodiode.