dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis procedure We analyzed our data by modeling response-type likelihood using logit mixed-effect models  (Jaeger, 2008). The random effects structure included random intercepts and slopes for all repeated measures factors (we had no between-subject factors). Analyses were conducted using the lme4  (Bates, Maechler, & Bolker, 2011)  and languageR libraries  (Baayen, 2011)  for the R statistics program (R Core Team, 2014). b values, standard errors, Z-values, and pvalues are shown in the tables accompanying the experiment together with R pseudo-code describing the models. Treatment and sum coding were used as appropriate and the reference levels are stated in the text. The Appendix shows raw means for each cell in the design. In all of our experiments we start with an analysis involving all of the data, in which we assessed withinexpression priming, between-expression priming and the interaction between the two.
2 ) and between-category trials (Panels 4-6 of Fig. 2 ) and conducted separate analyses on each. For the within-category analysis we tested a model with within-category group (3 levels: some ? some, number4 ? number4, ad hoc ? ad hoc), and prime (2 levels: strong, weak) as factors, in which prime was coded with sum contrasts and target category with treatment contrasts (with ad hoc as reference). There was a significant effect of prime, b = 1. 24, p <. 001, Note. R-pseudo code shown in the first line of every section. Prime = priming factor (2 levels: strong, weak). WithBet = within/between factor (2 levels: within, between). WithCat = within expression category factor (3 levels: some, number4, ad hoc). Betcat = between expression category factor (3 levels: some M number4, some M ad hoc, number4 M ad hoc).
R-pseudo code shown in the first line of every section. Prime = priming factor (2 levels: strong, weak). WithBet = within/between factor (2 levels: within, between). WithCat = within expression category factor (3 levels: some, number4, number6, but restricted to number4, number6 for lexical boost analysis). Betcat = between expression category factor (3 levels: some M number4, some M number6, number4 M number6). PrimeCat = priming category factor (2 levels: number6, number4). (number4 M number6 as reference). We observed a significant between-category priming effect, b = 0. 52, p <. 001. However, we also observed significantly greater priming within number4 M number6 than within some M num-ber6, b = À0. 52, p <. 001, and numerically greater priming within number4 M number6 than some M number4, b = À0.
g. ,  Fox, 2006). Note. R-pseudo code shown in the first line of every section. Prime = priming factor (2 levels: strong, weak). WithBet = within/between factor (2 levels: within, between). WithCat = within expression category factor (3 levels: some, number4, plurals). Betcat = between expression category factor (3 levels: some M number4, some M plurals, number4 M plurals). BetPls = plurals factor (2 levels: plural combinations, non-plural combinations). effect of prime on plurals (plural M some and plural M number4), b = À0. 11, p =. 11. Overall we find no evidence that plurals and some/number prime each other.
Combined analysis of Experiments 1-3 We tested priming between some and four in all three experiments. Since sampling issues are likely to be minimal here (we used repeated measures manipulations throughout) we combined the experiments to create a larger (and more powerful) data set. We then used this data to investigate two further questions about how participants were completing our task. The first was whether the direction of priming was important, that is, whether some ? number4 results in dif-ferent priming effects than number4 ? some. Despite similarities, some differences remain between these two types of EVA: the EVA with numbers is harder to cancel and occurs more easily in a wider variety of linguistic environments than the EVA with some does. These differences may be tied to the way the alternatives are retrieved, such that retrieving the alternative of some would be a strictly more complex process than retrieving those for four, for instance.
Analysis and discussion Combining all three experiments resulted in a data set with N = 377 participants. The results are shown in Fig. 6. We first conducted an overview analysis with prime (weak, strong) and within/between as factors, as in previous analyses. This revealed the expected significant effects of prime, b = 0. 60, p <. 001, within/between, b = 0. 20, p <. 001, and the interaction, b = À0. 48, p <. 001 (Table  4  shows the statistical details). Simple effects analysis showed a significant effect of prime at the within category level, b = 1. 08, p <. 001, and the between category level, b = 0. 12, p <. 001, replicating our previous findings. Next we assessed the directional effects in the between category data. We constructed a model with prime (strong, weak) and direction (number4 ? some, some ? number4) as fixed effects, using sum coding, and applied the model to the between-category data.
This Note. R-pseudo code shown in the first line of every section. Factor expressions as in Table  1  except that levels are restricted to some and number4 and their combinations. Direction = prime-category ? target factor (2 levels: some ? number4, number4 ? some). Half = experiment half factor (2 levels: first half, second half). causes the participant to process the sentence more deeply, ultimately retrieving alternatives to what the speaker could have said and deriving the enrichment. On a weak prime trial, the weak interpretation provides a satisfactory response and there is no need to process the sentence more deeply. If the search mechanism has activation levels that retain their value across the inter-trial-interval, activation levels would be higher following a strong prime than a weak prime, and the enrichment would be more likely to arise.
During the rest of the course, participants had a chance to practice recognizing by themselves the different parts and attributes of flakes, blades, cores and retouched tools. The final remarks on what to do beyond description, databases, statistics and drawing occupied Sunday afternoon. It was clear that participants had difficulties in recognizing the different raw materials and alterations, although some of them were more interested in distinguishing different tool types in order to pass their exams. Participants who had already graduated and were actually working in archaeology were more interested in recognizing technical attributes and further implications of lithic technical analyses. Clarity in explanations and personalized attention contributed to participants' learning and improvement.
Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users' dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user's dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950.
The survey and responses were written in Standard German orthography as well as localized transcriptions and were collated, stored, and prepared for display on large paper maps  [3]. A cartographical structure for mapping dialectological evidence was born  [4]. A century and a half later, paper is being replaced by online surveys and smartphone applications (apps) as a very powerful and flexible medium for crowdsourcing language data. Internet-based crowdsourcing has recently emerged as a means of collecting language data in speech science  [5]. Amazon's Mechanical Turk  [6] , a key player in current crowdsourcing platforms, has been used extensively in recent years to collect data to develop the capabilities of human language technologies (for an overview paper see  [7] ). Labor that cannot yet be conducted by computers is crowdsourced via so-called 'Turkers'.
A more passive form of crowdsourcing, with subjects unaware of the scientific use of their data, is large-scale analysis of diatopic language variation using geotagged Twitter posts; one such study that used lexical parameters reports that the Spanish language is split into two superdialects, namely an urban variety used across major American and Spanish cities and a diverse form that encompasses rural areas and small towns  [14]. In this paper we demonstrate how crowdsourcing speech data with the smartphone app 'Dialäkt Äpp' (hereafter DÄ;  [15] ) allows documentation of language change and areal variation. DÄ's main function is the prediction of a user's home dialect location based on a 16-question survey pre-determined by phoneticians. Following the prediction, users can evaluate the result and indicate their real dialect location.
Underlying the 16 questions are 16 maps from the Linguistic Atlas of German-speaking Switzerland (Sprachatlas der Deutschen Schweiz)hereafter referred to as the Atlas-which documents the linguistic situation in German-speaking Switzerland roughly 70 years ago in 560 localities  [16]. Whether for educational or entertainment purposes, DÄ has been downloaded nearly 80,000 times. Feedback from nearly 60,000 users provides a contemporary snapshot of the Swiss German dialect landscape, which can be used to investigate diachronic variation by comparison with Atlas data  [17, 18, 19]. Upon its release, DÄ became the most downloaded free iPhone app in Switzerland  [17]. The app also received broad media attention in German-speaking Europe: it remained in the top three German educational apps in Switzerland for several months and it was covered by 20 Minuten, the most popular Swiss daily.
[25]  further revealed, unsurprisingly, that younger speakers were more likely to deviate from the Atlas than older speakers (cf. [27] ). In the present paper, we demonstrate that data collected through DÄ and subsequent diachronic analyses have the potential to shed considerable light on dialectal and linguistic diversity. In section 2 of this paper, we introduce how the app works and how we have applied it to the analysis of language change in German-speaking Switzerland. Section 3 shows results based on data elicited through DÄ, that are then discussed in section 4. A major part of the discussion is dedicated to highlighting the innovative nature of this approach to data collection and analysis while critically reflecting upon it as a dialect research methodology. Section 5 introduces the quality control we applied on the results presented.
Comparing Atlas with DÄ data In order to conduct analyses of language change on the basis of these data, we need users to provide feedback on the predicted dialect. If they believe the first locality in the result list to be accurate, users are shown a new screen informing them about how to support our research. By clicking on 'OK, I'd like to help', users are prompted to indicate age and gender. Having done so, they once again verify their voluntary participation in our study by clicking on 'send' to submit their data. In doing so they consent to sending off their data on variant selection, dialectal origin, gender, and age. This is explained on the screen. None of these pieces of information individually or in combination allow for identification of a user in the database. Users also have the opportunity to decline in the first instance, in which case they are shown the results screen again (Fig  3 , left and center left).
38% of the Swiss German-speaking population per canton (median = 1. 45%). The figure ranges from nearly 2% in Zug and 1. 8% in Glarus to 1. 1% in Thurgau and 1% in Basel-Landschaft. Localities. The 550 predicted localities are based on those used in the Atlas (see 2. 3). Descriptive statistics reveal that three places from the Atlas were not represented in the DÄ dataset at all: Mutten (GR), Obergoms (VS), and Sternenberg (ZH). For every other locality there was at least one respondent. On average, we observe 107 users per locality (median = 48). Fig  5  shows the number of respondents per locality. We observe that the majority of users are found in the more densely populated Swiss Central Plateau, in the north. The southern, more mountainous localities provided fewer respondents. Gender. Of the users, 42% (n = 24,654) were females and 58% (n = 34,269) were males.
Language change Mismatches between the Atlas data and self-reported data from the app suggests linguistic change in progress, i. e. that a dialect has changed between the Atlas and now. The reported results therefore point to significant language change over the past 70 years, whether on a phonetic, lexical, or morphological level. We can speculate as to the causes of this change. Greater speaker mobility increases the range of variants that speakers may come into contact with, favoring the transmission chances of more frequently used and geographically more widespread variants over rarer and more isolated forms  [47]. Swiss German speakers' increased tendency to send text messages and contribute to social media in dialect may also increase speakers' contact with a diverse range of linguistic variants  [25].
2). • There are other methodological concerns with using crowdsourced data that deserve mention (see 4. 5. 3). Data elicitation: The Atlas data were collected using a direct method. Researchers went into the field, conducted interviews, and had subjects fill out questionnaires. The Atlas was generated based on the answers provided. DÄ data were collected indirectly, with no researcher present. There is, therefore, much less control over how the data were elicited in the indirect method. One could argue, however, that one advantage of the app-based corpus is that every user receives essentially the same stimulus to respond to, whereas in the Atlas (as in other similar dialect atlas projects with multiple fieldworkers), it is possible that different data collectors administered the task slightly differently-and transcribed the speakers' variants slightly differently.
Also, subjects are distracted very easily  [9] : they may have other applications running on their phones or possibly receive e-mail alerts or instant-messages. This type of experiment contrasts with listening tests conducted in laboratories, typically sound-treated rooms with state-of-the-art equipment, where stimuli are delivered undistorted to the listeners. One upside of these 'unfavorable' effects when testing with the app environment is, however, that all these factors increase the validity of the results: the data represent the type of hearing performance achieved in day-to-day life. Consequently, these findings are likely to generalize better to a greater range of real-world situations (e. g. [58] ). Moreover, listeners are known to vary in their perceptual performance: such variation is largely due to differences in exposure to dialects, metalinguistic awareness, age, hearing, and for some tasks, personality and educational factors  [59].
When a researcher conducts a perception test, it is common practice to seek a homogeneous group of participants meeting welldefined selection criteria. Criteria that are normally controlled for include gender, age, educational level, language history and normality of hearing. Controlling for hearing, for example, is critical because many people with mild or even moderate hearing loss are unaware of their deficit  [59]. Upholding such criteria is difficult with a design such as the one used in DÄ. Multiple submissions: In laboratory research, subjects typically only submit their data once, while app-or web-based research allows for multiple submissions  [60]. There are different scenarios that may lead to multiple submissions: (a) the same person uses the same app to participate repeatedly or (b) the same person uses the app on different smartphones to participate repeatedly.
Conclusion DÄ capitalizes on the Swiss public interest in dialectology by providing functionality that allows users to localize their own Swiss German dialect on the basis of a few words. We built a model with a set of maximally predictive words that were chosen from the historic Linguistic Atlas of German-speaking Switzerland, which documents the language situation of the mid-20 th century. These words highlight the differences between dialect areas. Given that we elicited the user's dialect with the app, we were able to compare old Atlas dialect data to data from 2013/2014, allowing us to locate and document language change. We report that changes have taken place on all investigated linguistic levels: phonetic, lexical, and morphological. Results further show a trend that phonetic variables seem less vulnerable to change, while lexical and morphological variables diverge from the Atlas findings to a greater degree.
Effect of increasing X-ray dose on aDNA molecule length. In living organisms X-ray radiation is known to induce double strand breaks (DSBs)  22. Average DNA molecule length is thus assumed to become shorter after radiation exposure. To test if the same effect applies to ancient molecules, we performed mitochondrial capture  23  and sequenced all enriched libraries from samples that were previously exposed to dose from 0 to 43. 5 kGy. No DNA could be sequenced from the 1000 s (93. 72 kGy) sample. Sequence data were pre-processed, filtered, and mapped to the mitochondrial reference sequences of the corresponding organisms. Between 4,208,541 and 2,425 fragments mapped to the mitochondrial DNA (mtDNA) reference sequences of the corresponding organism. Mean fragment lengths were determined for mapped (endogenous) and overall (including non-mapped) fragments, and samples were again normalised as described above.
To measure the effect of different X-ray doses on nucleotide misincorporation patterns, C to T substitution frequencies were measured for the first position from the 5' end from the DNA sequence of the exposed aliquots. Data were normalised by the corresponding non-scanned control aliquots and plotted against radiation dose (Fig. 4b ). Predictably, substitution frequencies correlate negatively with exposure time, suggesting that a higher X-ray dose lowers the amount of C to T nucleotide misincorporations. This is best explained by DNA strand breaks induced by radiation that introduce new 5' ends, thus lowering the overall proportion of molecules with terminal C to T substitutions.
We performed dosimetry of the most relevant configurations used for μ CT on sub-fossils with both conventional and synchrotron sources. Dosimetry on imaging systems was performed in three different steps: classical synchrotron experiments, low dose synchrotron experiments and conventional microtomographs. All measurements and synthesis of results can be found in Supplementary Data 1 and Supplementary Data 2. By compiling all the measurements obtained for the conventional microtomographs, we propose a water equivalent surface dose estimator relevant for most of the conventional X-ray imaging systems used to image sub-fossils (Supplementary Data 3). (a) aDNA quantitation after exposure to 720 Gy during a real synchrotron μ CT scan. "scan": number of aDNA molecules after scanning the aliquot with 720 Gy, "no scan": non-scanned control.
• Always perform a precise collimation of the beam (beam size fitting to the field of view) to ensure that a dose as low as possible is delivered to non-imaged areas. • For sub-μ m resolution scans, orientate the samples carefully to minimise the amount of material crossing the beam path. Recommendations for sub-fossil scanning in general. • Never perform μ CT imaging on wet specimens, such as frozen samples, and samples that were recently cleaned with water. • Do not put sub-fossil samples into water (e. g. to reduce beam hardening) when performing a μ CT scan. • Do not perform scans at higher resolution than necessary, as the dose increases roughly at the square power of the increase of resolution. • Dose is cumulative: do not perform multiple scans when similar data already exist. Data sharing (under responsibility of the curators) and public repository databases play an important role on this.
Ranges of parameters explained below include conditions where the dose rate was not measurable in some cases, but this clearly appears in Supplementary Data 1. For the BIR, three sessions of measuring were run. Firstly, the dosimeter was fixed on the sample stage at 250 mm from the X-ray source, initially with no metallic filter and then with various filters (0. 25 mm brass, 0. 5 mm brass, 1 mm brass, 1 mm aluminium), the operators were then able to measure the dose rate for power ranging from 5 to 150 W and voltage from 50 to 200 kV. Secondly, for each filtering condition mentioned above, the dose rate was measured at 50 kV, 130 kV and 200 kV and placing the dosimeter at an increasing distance from the dosimeter from 20 mm to 500 mm. Last, the influence of distance to beam axis was tested at 130 kV, 50 W, 250 mm from the X-ray source, and with 0.
Quantitation of aDNA Amount. qPCR quantification of the libraries was carried out on a Roche LightCycler 480 using the DyNAmo HS SYBR Green qPCR kit by Thermo Scientific. qPCR primers and standards are described in Meyer and Kircher 2010  39. All DNA quantities were numbered as a number of copies -i. e. a number of fragments with an adapter on each end -per μ l of library. As there is no amplification during library preparation, variation in the number of copies between libraries is assumed to reflect the variation in the DNA quantity between extracts. The number of copies for each library was normalised by the amount of extracted material in order to prevent a bias due to sampling. Results were analysed using the Roche Lightcycler's 480 integrated software, Microsoft Excel and the R statistical software  41.
The obtained values were normalised by division through the corresponding value of the non-scanned control aliquot. Except for the last experiment, mean values were calculated per scan group consisting of two aliquots from independent sequencing libraries. See Supplementary Data 4 for normalised DNA quantitations in figures 1, 2 and 5a. Enrichment of aDNA. Target enrichment of mtDNA was performed by capture of the pooled libraries using bait generated from roe deer (Capreolus capreolus), brown bear (Ursus arctos) and bison (Bison bison) mtDNA  23. The bait was generated by use of three primer sets (Table  2 ) designed with the Primer3Plus software package  42. All extractions and pre-amplification steps of the library preparation were performed in clean room facilities and negative controls were included for each reaction.
Duplicate removal was performed on those reads that showed identical start and end coordinates only by using a custom software now integrated into the EAGER-pipeline  46. The produced BAM files were filtered for sequences with a mapping quality of at least 20. We set a threshold of at least 1000 mapped reads for an aliquot to be considered in further analyses. Fragment Length Distribution Analysis. Read lengths were obtained from quality filtered BAM files using SAMtools  47. Read length means and boxplots were obtained using the R statistical software  41. Significance testing was assessed using the unpaired Student's t-test in R. Normalisation of the data was achieved by dividing the obtained mean values by the mean fragment length of the corresponding non-scanned control aliquots. Scatter plots for regression analysis were generated in Microsoft Excel.