dataesr/scientific-paragraphs-categorization huggingface dataset:
We feed our qualitative analysis from different sources applying a combination of methods. We were participants of a community of stakeholders from research, the public and the humanitarian sector working in the area of improving information and data management for decision-making in the area of climate risk management and food security in Guatemala during various years. Being participants as observers allowed us to get nuanced and context-specific qualitative information through participation in meetings, conferences, roundtables, and personal interaction. We base our analysis on personal notes, protocols, and reports. In the course of different studies, we applied semi-structured expert interviews, focus group discussions and literature and content analysis, (for a detailed explanation of the different unstructured and semi-structured datasets and the approach to analysis please refer to  Müller at al.
, 2019). No specific selection criteria for beneficiaries of public subsidies were in place. This reflects what has been found for the fertilizer program: broad selection criteria were defined, but there was no evidence on how these criteria were operationalized to guide the definition of beneficiaries (ASIES, 2012). In the case of drought damage and loss, official data were requested to define beneficiaries of government support, but there was no guideline on how to generate this data. So, extension agents or extension units were developing their own data collection and analysis method  (Müller et al. , 2019). The commonly used paper-based data collection schemes by the extension system leave room for influence on the collected field data. Superiors check and enter the data into databases that are then used to support the definition of beneficiaries.
transparency in data publication leave space for speculation. Many consulted stakeholders suspected that political interest was guiding the non-publication of the data. An important part of the context was that 2015 was an election year, that the president stepped down due to a corruption scandal, and that the government had very limited financial possibilities to respond to the drought. This specific year, different datasets hinting at the critical situation in agricultural production and food security were withheld by the government. The suspicion is that the government did not want their low response capacity to the crisis to reflect negatively on the ruling party during the election period. The paucity of data made it more difficult to hold politicians and governments accountable for their inaction.
Discussion and implications In the period from 2012 to 2015, the Guatemalan government has started to make important steps towards increasing transparency and accountability through an open government strategy. Certainbut not allgovernment data and information were made available. In the politicized and highly criticized fertilizer program, MAGA did refuse to publish full data on fertilizer distribution in an extremely political year that coincided with the presence of drought and high levels of food insecurity. This can be understood as an intent to evade accountability. The Guatemalan civil society had a strong suspicion towards the misuse of the program but could not make use of the existing data to prove the allegations (ASIES, 2012). Our quantitative analysis provides evidence that political interests influenced the decisions on fertilizer distribution in Guatemala.
For all this to happen, however, relevant data should be readily available in a usable format. A general user focus on decision-makers and users' needs, user context and data use skills helps to create relevant information and useful information systems (Principles for Digital Development Forum, 2019). To achieve this, governmental policies and donor support should aim at the following goals. a. Government policy should assure access (if possible unrestricted) to data and information through a user-friendly website design and updated information on where to get offline data resources; b. The government should ensure streamlining and harmonizing data formats and codes in official data, and publish metadata, with a focus on interoperability of data. Data curation or the ability to link datasets from different sources is important for generating information for policy design, implementation and monitoring (OECD, 2019); c.
Acknowledgements We acknowledge support from the  Climate Change, Agriculture and Food Security (CCAFS) Programme , under the project  P42   Agroclimas 1 -Tailored agroclimatic and food security information for decision making in Latin America  and  P1604   Agroclimas 2 -Digitally integrated approaches for managing climate risks and increasing food security  (http://bit. ly/2i3V0Nh). CCAFS is carried out with support from  CGIAR Trust Fund Donors  and through bilateral funding agreements. For details please visit https://ccafs. cgiar. org/donors. The views expressed in this paper cannot be taken to reflect the official opinions of these organizations. We also acknowledge the support from the  Inter-American Institute for Global Change Research  under the grant number  CRN3-107  for this research project.
Education, training and isotopes production are the most important uses of the Moroccan 2 MW TRIGA Mark II reactor situated at the National Center for Energy Sciences and Nuclear Techniques (CNESTEN, Morocco). To develop new R&D projects in research reactors, the particular and advanced knowledge of neutron and photon flux distribution, within and around the reactor core, is crucial. In order to precisely preparing the experiments in the CNESTEN's TRIGA reactor, a detailed model was developed using the 3D continuous energy Monte Carlo code TRIPOLI-4 and the continuous energy cross-section data from the JEFF3. 1. 1 nuclear data library. This new model was used to carry out preliminary neutron and photon calculations to estimate flux levels in the irradiation channels as well as to calculate kinetic parameters of the reactor, core excess reactivity, integral control rods worth and power peaking factors.
Continuous-wave functional near-infrared spectroscopy (fNIRS) neuroimaging provides an estimate of relative changes in oxygenated and de-oxygenated hemoglobin content, from which regional neural activity is inferred. The relation between both signals is governed by neurovascular coupling mechanisms. However, the magnitude of concentration changes and the contribution of noise sources to each chromophore is unique. Subsequently, it is not apparent if either chromophore signal practically provides greater information about the underlying neural state and relation to an experimental condition. To assess this question objectively, we applied a machine-learning approach to four datasets and evaluated which hemoglobin signal best differentiated between experimental conditions. To further ensure the objective nature of the analysis, the algorithm utilized all samples from the epoched data rather than pre-selected features.
Methods We quantified the performance of different signal chromophores and signal processing to differentiate between experimental conditions from four datasets, three of which are publicly available. To ensure the conclusions from this study were generalizable, the datasets were selected to cover a variety of brain regions (occipital, motor cortex, auditory cortex), different task types (passive vs. active responses), and different stimulus modalities (auditory and visual). Further, canonical experimental designs were included, such as finger tapping and visual checkerboards, as well as more challenging experimental tasks for the fNIRS modality, such as detecting auditory-evoked responses (which is challenging due to the neural structures of interest lying relatively distant from the location of optodes on the scalp).
Datasets Each dataset is briefly described here in turn. Complete details of the experimental setup and recording parameters are provided in the associated publications  (Luke et al. , 2021a; Luke and McAlpine, 2021; Shader et al. , 2021). All data were collected using NIRx NIRScout devices, although the exact device, optode placement, and sample rate varied across each dataset. All measurements included an additional eight source-detector pairs with a separation distance of 8 mm (short-distance channels) designed specifically to measure extracerebral changes in oxygenated and de-oxygenated hemoglobin. All experiments utilized a block design with randomized inter-stimulus-interval durations.
Finger Tapping In this active experimental task, five participants were asked to tap their thumb to fingers on the same hand when they heard an auditory cue  (Luke and McAlpine, 2021). Participants used either their right or left hand only during any experimental block, and the hand used was indicated by a sound. If the sound was presented in the left earphone, participants tapped with their left hand, and similarly, if in the right earphone, participants tapped with their right hand. Participants tapped their thumb to fingers for the 5 sec duration of the sound. Each condition, either right-or left-handed tapping, was presented 30 times in a random order with a random inter-stimulus interval of 15-30 seconds. Optodes were placed over the motor cortex (Figure  1  top left). To evaluate model performance using this dataset, the ability to differentiate between left-hand and right-hand tapping based on the fNIRS measurement was evaluated.
Auditory Speech In this passive experimental task, seventeen participants were asked to watch a silent subtitled movie of their choice. Simultaneously, over headphones, they were presented with either speech, low-frequency noise, or silence in a randomized experimental order  (Luke et al. , 2021a). Each stimulus was presented for 5 sec with an inter-stimulus interval of 15-30 seconds, and each was repeated 20 times in a randomized order. Optodes were placed over the left inferior frontal gyrus, visual cortex, and auditory cortices (Figure  1  bottom left). To evaluate model performance using this dataset, the ability to differentiate between auditory speech and silence condition based on the fNIRS measurement was evaluated.
Auditory vs Visual Speech In this active experimental task, eight participants were asked to pay attention to a running story that was presented in short segments in a block paradigm. Each experimental block presented the story segment either as auditory only or visual only (a face presented on a monitor speaking the sentence, without sound)  (Shader et al. , 2021). The order of conditions was randomized per participant and 18 trials were presented of each condition; the average trial duration was 12. 5 seconds. Optodes were placed over the left inferior frontal gyrus, visual cortex, and auditory cortices (Figure  1  bottom right). To evaluate model performance using this dataset, the ability to differentiate between the auditory-only and visual-only conditions based on the fNIRS measurement was evaluated.
Analysis All analyses were performed using MNE v0. 24  (Gramfort et al. , 2013; Gramfort et al. , 2014) , MNE-NIRS v0. 1. 1  (Luke et al. , 2021a) , and scikit-learn v1. 0  (Pedregosa et al. , 2011). The same pre-processing was applied to each dataset a minimal processing pipeline was applied to each measurement. Several additional pre-processing steps were also evaluated. An example of the pipeline is provided on the MNE-NIRS web page. After data collection, all data was converted to BIDS format  (Gorgolewski et al. , 2016)  using MNE-BIDS  (Appelhoff et al. , 2019). The signal was first downsampled to 1. 5 Hz, then converted to optical density, and then hemoglobin concentration using the modified Beer-Lambert Law with a differential pathlength of 6. A low-pass filter was applied to the data with a cut-off frequency of 0.
6 Hz and transition bandwidth of 0. 1 Hz. This signal was restructured into epochs from 5 seconds before to 30 seconds after stimulus onset. The stimulus duration was different across datasets, however, a fixed post-onset epoching time was selected to ensure analysis was consistent across all data. Each epoch was baseline corrected based on the pre-stimulus time segment and a linear detrend was applied to each epoch. Epochs with a peak-to-peak amplitude greater than 100 μM were rejected. The epoched data were utilized in the machine-learning procedure. Two machine-learning metrics were extracted from each dataset. For qualitative analysis, a time-by-time decoding approach was used to view the decoding performance at each sample in the epoched data. Second, for quantitative analysis, a single spatio-temporal metric approach was used that simultaneously used all channels and time points to estimate the experimental condition.
, 2010)  (HbO & HbR w/ enhancement), and 5) total hemoglobin (HbT; I. e. , HbO + HbR). Thus, for each component of the fNIRS signal, an estimate of the ability to differentiate between conditions was obtained. To compare the performance of HbO and HbR, a twosided paired t-test was performed comparing the average performance per dataset.
Results & Discussion To determine which component of the fNIRS signal provided the greatest information of the brain state related to experimental condition, a machine-learning analysis was applied to various chromophore and combinations of chromophore signals. The ability to differentiate between conditions was above chance for all datasets and all fNIRS signal components. The decoding performance for the pre-stimulus period was at chance level for all datasets (Figure  2 ), demonstrating correct implementation of the decoding procedure. The time-by-time decoding performance peaked within the first 10 seconds post stimulusonset for finger tapping, checkerboard, and speech datasets, consistent with the 5-sec stimulus duration and known temporal characteristics of the hemodynamic response  (Glover, 1999; Tak and Ye, 2014).
Whereas the decoding performance for the auditory-visual dataset maintained its peak decoding performance for approximately 20 sec, consistent with the longer ~12. 5 sec stimulus. The optimal decoding time was typically following the offset of the stimulus, and at or a few seconds after the peak in the oxyhemoglobin response (Figure  3 ). This illustrates that manual selection of signal features, such as signal value at the end of stimulus/task duration or windowed mean values over a stimulus duration, may not capture the optimal decoding which peaks several seconds post stimulus offset. The decoding performance did not return to baseline within 30 seconds of the stimulusonset for all datasets. This is likely due to the epoch length overlapping with the following stimulus presentation, with a 5-sec stimulus and minimum ISI of 15 sec; the next stimulus may occur as early as 20 seconds.
However, the 30 second duration of post-stimulus epoching was selected to allow identical processing to be applied to each dataset. CC-BY-NC-ND 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted November 20, 2021. ; https://doi. org/10. 1101 /2021. 11. 19. 469225 doi: bioRxiv preprint. 11. 19. 469225 doi: bioRxiv preprint  The oxygenated hemoglobin signal provided the best performance (Table  1 ), outperforming both the de-oxyhemoglobin and total hemoglobin signals. The mean ROC-AUC was greatest for the HbO data in all four datasets and was larger than the HbR data (M=7. 7, SD=2. 3), t(3)=5. 6, p=.
Results indicate that including de-oxygenated hemoglobin as input in addition to the oxygenated hemoglobin signal to the machine learning model provides no performance improvement in decoding between experimental conditions based on fNIRS neuroimaging signals. Methods to enhance signal quality based on the anti-correlated relationship between oxyand de-oxygenated hemoglobin signals  (Cui et al. , 2010)  are widely reported. However, our analysis suggests that applying this technique to fNIRS data provides for no improvement in discriminating between brain states; indeed it reduces performance in three of four of the datasets compared to when oxygenated hemoglobin signals alone are assessed. This indicates that the benefits of applying this algorithm to fNIRS measurements are largely cosmetic and may not provide any objective improvement to signal quality.
By utilizing datasets that measured changes in oxygenation from multiple brain regions, we demonstrated that the oxygenated hemoglobin signal provides the greatest information about neural state related to the stimulus/task compared to de-oxygenated hemoglobin and total hemoglobin. Evaluating the decoding performance from different brain regions is necessary as the systemic signal contribution is not heterogenous over the head, with greater contamination from Mayer waves and pial veins in the motor cortex region  (Gagnon et al. , 2012). Additionally, the relative contribution of neural activity to the measured signal varies across the head in fNIRS measurements due to variation in skin and skull thickness, and variations in relative depth of the neural tissue of interest. Despite these differences, we observed consistent benefit to using the oxygenated hemoglobin signal alone for decoding between experimental conditions.
Conclusion Utilizing multiple datasets, we demonstrate that the fNIRS signal generated by oxygenated hemoglobin provides greater information about the brain state related to the experimental condition than the signal generated by de-oxygenated hemoglobin, regardless of brain region, stimulus, or task. Including manually generated linear and non-linear combinations of the oxy-and de-oxygenated signals did not improve the performance of machine-learning decoding beyond using the oxygenated hemoglobin signal alone. This suggests that the deoxygenated hemoglobin signal provides no independent information beyond that provided.
the diaries/daily life reports of the Dominican priory and 20 The EBAF photographs are not part of the recent initiative by the BNF (National French Library) Bibliothèques d'Orient, like many other missionary congregations, though A. Jaussen is mentioned as an important actor, "Antonin  Jaussen (1871 Jaussen ( -1962)) ," Bibliothèques d'Orient. activities). They can function as meta-data and allow a finer view of the photographers and the context of their approaches. The potential users would be able to define the search by time, place and theme, sorting the results according to the categories, descriptors and keywords used when photographs were scanned and added to the record. Later on, the viewers familiar with the time, place and people involved would be able to contribute information that the EBAF could consider for incorporation into the electronic record.
61 Jaussen collected information during his surveys of Arab inscriptions in the city or at the St Joseph Sisters' dispensary. Part of the data also come from women's testimonials and interviews with Jaussen's privileged male interlocutors, in particular sheikhs. The latter potentially take up the fantasy projections of female socialisation by men in the city, but was an attempt to overcome the methodological limitations imposed by the fieldwork. The photographic plates of the book echo the focus given to women. Female portraits were commonplace for photographers at the time. One thinks in particular of Bonfils' portrait of a woman from Nablus -very likely a model that posed -representing a young woman sitting, dressed for the occasion, the lower part of her face covered by a veil. Postcards62 and family portraits63 were also widely used to depict women, with different perspectives and objectives.
On the other hand, only 137 contigs were coassembled from soil microcosms, and the maximum alignment rate obtained when mapping metagenomic reads against the contigs was 0. 08%. No ARGs were identified when blasting the contigs against the CARD database and filtering at 60% identity and 33 amino acid length. Two genes that encode for components of multidrug efflux pumps, muxB and adeF, were found to significantly increase their relative abundance in nonassembled metagenomic sequences from bacterial enrichments polluted at 12 µg/mL of gentamicin (Table  1 ). However, none of these efflux pumps were related to gentamicin efflux. Thus, although gentamicin causes significant effects on the bacterial communities of soil bacterial enrichments, a selection for genes conferring resistance to gentamicin was not obvious.
Determination of Gentamicin Effect on Soil Enrichments First, in order to determine whether gentamicin had an effect on the enriched fraction of soil bacteria in liquid enrichments and which gentamicin concentrations were subinhibitory at a cursory level, soil bacteria were extracted in 0. 9% NaCl solution, and 150 µl of 1:10 tryptic soy broth (TSB) medium containing soil bacteria without antibiotics or with gentamicin at 0, 0. 01, 0. 05, 0. 1, 0. 5 or 11 µg/mL were transferred to a 96-well culture plate and incubated for 24 h at 29 • C under continuous shaking in the MultiSkan GO Plate Reader (Thermo Scientific, Waltham, MA, USA). Optical density at 600 nm (OD 600 ) was measured every hour over the 24-h incubation. Then, ANOVA tests and Student's t-tests between the OD 600 measured at each gentamicin concentration after 24-h incubation and the average between all groups were performed using the ggpubr package in R.
Melting curves were generated after amplification by increasing the temperature from 60 • C to 95 • C. The number of copies per µL of reaction obtained from the amplification of aac(  6 ) genes was normalized by the copies of the 16S rRNA gene per µL of reaction to assess their relative abundance in soil microcosms and bacterial enrichments. Then, ANOVA tests and Student's t-tests between the DNA concentration, copies of 16S rRNA gene per µl of reaction and relative abundance of aac(6 ) genes measured from each group (each gentamicin concentration after 24-h exposure for bacterial enrichments and each gentamicin concentration and each exposure time for microcosms) and the average between all groups were carried out using the ggpubr package in R.
DNA libraries were prepared from amplified products based on Illumina's 16S Metagenomics Library Prep Guide (15044223 Rev. B) using the Platinum Taq DNA Polymerase (Invitrogen, Carlsbad, CA, USA) and the Nextera XT Index Kit V2 (Illumina, San Diego, CA, USA). DNA sequencing with a 15% PhiX spike-in was performed using the MiSeq System and the MiSeq Reagent Kit v2 (Illumina). Reads were trimmed to meet a quality score of Q20. Then, pair-ended reads were assembled using PANDAseq  [46]  at a sequence length between 410 and 500 bp and an overlap length between 20 and 100 bp, using the rdp_mle algorithm. Finally, each of the DNA sequences was annotated to the genus level using the Ribosome Data Project (RDP) database and the RDP Bayesian classifier using an assignment confidence cut-off of 0. 6  [47].
Three microcosm samples were excluded from further analyses due to insufficient sequencing depth: a triplicate polluted at 1 µg/g of gentamicin after 0-day exposure, a triplicate polluted at 1 µg/g of gentamicin after 2-day exposure and a non-polluted triplicate after 8-day exposure. The genera that had less than 10 associated sequences in the ensemble of sequences from enriched media and soil microcosms were removed. Then, the relative abundances of the remaining genera were calculated and plotted individually. PCoA analyses were performed using R to compare the community composition of soil microcosms and bacterial enrichments. In addition, the genus richness measured in each sample was determined using the vegan package in R  [48]. Then, ANOVA tests and Student's t-tests between the genus richness detected in each group (each gentamicin concentration after 24-h exposure for bacterial enrichments and each gentamicin concentration and each exposure time for microcosms) and the average between all groups were carried out using the ggpubr package in R.