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Our dataset is focused on automatic voice recognition for the purpose of diagnosing language disorders; hence, our dataset consists of written documents. As a whole, our study dataset comprises of inquiries, assertive speech, and responses to all three. Young children are being asked these questions and taught these stories. Our focus is on children aged 0 to 6 years. We have polled numerous children within this age range with the permission of their families, including infants from our own family, extended family, neighbors, non-governmental organizations (NGOs) in Bangladesh who work with children, hospitals, and many more. The surveys focused mostly on eliciting responses from the children, whether via direct questioning or age-appropriate aggressive language. Any action or expression of approval in response to the question or statement counts as an answer. Children of varying ages reach several stages in their development of language. A baby who is 7-12 months old may utilize babbling consonant-vowel combinations and consonant sounds; a baby who is 12-20 months old may use gestures, identify their own name, etc. Prior to the experiment, we required to do an analysis of the dataset. Age, Speech in Bangla, Speech Translated in English, Response in Bangla, Response Translated in English, and label are the 6 columns that make up our dataset. We gathered information from kids as young as one month old and as old as sixty (60) months old. In addition, we can see that the number of normal samples is about twice as large as the number of impaired samples (out of 252 total samples. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Cross Key

fMRI data from participants performing a visual sequence recall task. The data is in BIDS 1.0.1 format but each participant's data is compressed into a single archive following a 'sub-*id*.tar.gz' pattern. For each participant there are three sub-folders: (1) 'anat' -- T1-weighted anatomical image. (2) 'func' -- (a) EPI BOLD images (multiband factor 2), (b) event timings data for contrast regressors (.tsv files). (3) 'fmap' -- EPI images in opposite phase-encoding direction to 'func' images to derive inhomogeneity field maps. For full details on the file formats included see the BIDS specification at https://bids-specification.readthedocs.io/en/stable/ The MRI data has been fully anonymised: all information linking the participants to the MRI scans has been removed. This included 'de-facing' where all facial features are removed from the images to ensure a greater degree of anonymity for data sharing purposes. De-facing was performed with 'pydeface' package (https://github.com/poldracklab/pydeface). The data was acquired at the Medical Research Council Cognition and Brain Sciences Unit (Cambridge, UK) on a 3T Siemens Prisma MRI scanner using a 32-channel head coil and simultaneous multi-slice data acquisition. Functional images were collected using 32 slices covering the whole brain (slice thickness 2 mm, in-plane resolution 2��2 mm) with acquisition time of 1.206 seconds, echo time of 30ms, and flip angle of 74 degrees. Each participant performed two scanning runs and 510 scans were acquired per run. The initial ten volumes from the run were discarded to allow for T1 equilibration effects. Stimulus presentation was controlled by PsychToolbox software: the trials were rear projected onto a translucent screen outside the bore of the magnet and viewed via a mirror system attached to the head coil. For full details of acquisition see 'Sequence learning recodes cortical representations instead of strengthening initial ones' by Kalm K, Norris D. PLOS Computational Biology, 2021. Analysis scripts for the data are available at: https://gitlab.com/kristjankalm/fmri_seq_ltm

This dataset contains key characteristics about the data described in the Data Descriptor The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

This dataset contains key characteristics about the data described in the Data Descriptor The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

Tools and services evaluation speadsheet

Appendices

The marine pollen record from NW Iberian margin sediments (core MD03-2697) covers the interval between 340 000 and 270 000 years ago, a time period centred on Marine Isotope Stage (MIS) 9. This dataset consists of pollen data enabling the documentation of vegetation changes in the north-western Iberian Peninsula and therefore the terrestrial climatic variability at orbital and in particular at millennial scales during MIS 9, directly on a marine stratigraphy.

Yongey Mingyur Rinpoche (YMR) is a Tibetan Buddhist monk, and renowned meditation practitioner and teacher who has spent an extraordinary number of hours of his life meditating. The brain-aging profile of this expert meditator in comparison to a control population was examined using a machine learning framework, which estimates “brain-age” from brain imaging. YMR’s brain-aging rate appeared slower than that of controls suggesting early maturation and delayed aging. At 41 years, his brain resembled that of a 33-year-old. Specific regional changes did not differentiate YMR from controls, suggesting that the brain-aging differences may arise from coordinated changes spread throughout the gray matter.

Presence of Common Scales: country: name of the country or the territory; geodi: two-letters country code; iso: three-letters country code; d: date of the observation; cases: number of cases reported on the given day by the European Centre for Disease Prevention and Control; deaths: number of deaths reported on the given day by the European Centre for Disease Prevention and Control; school: binary variable equal to1 if schools were closed and 0 otherwise; school_local: binary flag to distinguish localized school closures from other cases. 1 denotes that school closures were implemented at the local level and 0 denotes that school closures were not implemented at the local level (either at the national level or no school closures). The data on the scale of school closures is imported from the UNESCO. The interaction of school and school_local allows researchers to create three levels of measures: no school closures (school=0 and school_local=0), localized school closures (school=1 and school_local==1) or national school closures (school=1 and school_local=0); domestic: binary variable equal to 1 if there was a domestic lockdown and 0 otherwise; domestic_local: binary variable to distinguish localized domestic lockdowns from other cases. 1 denotes that domestic lockdowns were implemented at the local level and 0 means that domestic lockdowns were not implemented at the local-level (either at the national level or not implemented). The nature of the domestic lockdown is based on our reading of the measures reported by the ACAPS. The interaction of domestic and domestic_local allows researchers to create three levels of measures: no domestic lockdown (domestic=0 and domestic_local=0), localized domestic lockdowns (domestic=1 and domestic_local=1) or national domestic lockdowns (domestic=1 and domestic_local=0); travel: binary variable equal to1 if travel restrictions were implemented and 0 otherwise; travel_partial: binary flag to differentiate partial travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no travel restrictions (travel=0 and travel_partial=0), partial travel restrictions (travel=1 and travel_partial=1) or strict travel restrictions (travel=1 and travel_partial=0); travel_dom: binary variable equal to1 if travel restrictions within the country (e.g. inter-region travels) were implemented and 0 otherwise; travel_dom_partial: binary flag to differentiate partial domestic travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no domestic travel restrictions (travel_dom=0 and travel_dom_partial=0), partial domestic travel restrictions (travel_dom=1 and travel_dom_partial=1) or strict domestic travel restrictions (travel_dom=1 and travel_dom_partial=0); curf: binary variable equal to1 if a curfew was implemented and 0 otherwise; curf_partial: binary flag to differentiate partial curfews from other cases. 1 denotes that the curfew was partial and 0 denotes that the curfew was not partial (either strict or not implemented). The nature of the curfew is based on our reading of the measures reported by the ACAPS. The interaction of curf and curf_partial allows researchers to create three levels of measures: no curfew (curf=0 and curf_partial=0), partial curfew (curf=1 and curf_partial=1) or strict curfew (curf=1 and curf_partial=0); mass: binary variable equal to1 if bans on mass gatherings were implemented and 0 otherwise; mass_partial: binary flag to distinguish localized bans on mass gatherings from other cases. 1 denotes that bans on mass gatherings were partial and 0 denotes that bans on mass gatherings were not partial (either strict or not implemented). The nature of the bans on mass gatherings is based on our reading of the measures reported by the ACAPS. The interaction of mass and mass_partial allows researchers to create three levels of measures: no bans on mass gatherings (mass=0 and mass_partial=0), localized or partial bans (mass=1 and mass_partial=1) or national or strict bans (mass=1 and mass_partial=0); elect: binary variable equal to1 if some elections were postponed and 0 otherwise; elect_partial: binary flag to differentiate countries which postponed only some of the elections from the others. 1 denotes that countries both maintained and postponed elections and 0 denotes that elections were either postponed, maintained or were not scheduled. IDEA lists all maintained and postponed elections since the beginning of 2020. The interaction of elect and elect_partial allows researchers to differentiate three settings: all elections were maintained despite COVID-19 (elect=0 and elect_partial=0), some elections were maintained and others were postponed (elect=1 and elect_partial=1) or all elections were postponed (elect=1 and elect_partial=0); sport: binary variable equal to1 if bans on sporting and large events were implemented and 0 otherwise; sport_partial: binary flag to distinguish partial bans and cancellations of sporting and large events. 1 denotes that bans on sporting and large events were localized, strict or with no spectators, 0 that bans on sporting and large events are not localized or partial (either national or no measures implemented). The nature of the bans on sporting and large events is based on our reading of the measures reported by the ACAPS. The interaction of sport and sport_partial allows researchers to create three levels of measures: no bans (sport=0 and sport_partial=0), partial bans (sport=1 and sport_partial=1) or national bans on mass gatherings (sport=1 and sport_partial=0); rest: binary variable equal to1 if restaurants were closed and 0 otherwise; rest_local: binary flag to distinguish localized and/or partial restaurant and bar closures from other cases. The variable is coded 1 in the three following situations: localized closures, limitations on the number of customers in bars and restaurants, and closures of either bars or restaurants. 0 indicates national closures or no closures at all. The coding is based on our reading of the measures reported by the ACAPS. The interaction of rest and rest_local allows researchers to create three levels of measures: no closures (rest=0 and rest_local=0), localized closures (rest=1 and rest_local=1) or national closures (rest=1 and rest_local=0); testing: binary variable equal to1 if there was a public testing policy and 0 otherwise; testing_narrow: binary flag to distinguish narrow testing policies from large testing policies. 1 denotes that testing policies were targeted to some individuals, 0 that testing policies were not targeted (either large or not implemented). The nature of the testing policy is based on the information reported in the measures ���mass population testing��� and ���testing policy��� in the ACAPS. When the measure was targeted, testing_narrow was coded 1. On the contrary, when the measure was not targeted, testing_narrow was coded 0. The interaction of testing and testing_narrow allows researchers to create three levels of measures: no testing policy (testing=0 and testing_narrow =0), narrow testing policy (testing=1 and testing_narrow =1) or large testing policy (testing=1 and testing_narrow =0); surveillance: binary variable equal to1 if mobile app or bracelet surveillance was implemented and 0 otherwise; surveillance_partial: binary variable equal to1 if the enhanced surveillance is optional or reserved for a category of person (e.g. certain professions or foreigners) and 0 otherwise, based on information in the ACAPS. When the measure was partial, surveillance_partial was coded 1. On the contrary, when the measure was strict (anybody suspected of having COVID-19), surveillance_partial was coded 0. The interaction of surveillance and surveillance_partial allows researchers to create three levels of measures: no surveillance (surveillance=0 and surveillance_partial =0), partial surveillance (surveillance=1 and surveillance_partial=1) or strict surveillance (surveillance=1 and surveillance_partial =0); masks : binary variable equal to1 if mandates to wear masks in public spaces were implemented and 0 otherwise; masks_partial: binary variable equal to1 if the obligation to wear masks is regional, based on information in the ACAPS. When the measure was regional, masks_partial was coded 1. On the contrary, when the measure was national, masks_partial was coded 0. The interaction of masks and masks_partial allows researchers to create three levels of measures: no obligations to wear masks (masks=0 and masks_partial =0), regional obligations to wear masks (masks=1 and masks_partial=1) or national obligations to wear masks (masks=1 and masks_partial =0); state: binary variable equal to1 if the state of emergency is declared and 0 otherwise; state_partial: binary variable equal to1 if the state of emergency is declared on a local basis and 0 otherwise, based on information in the ACAPS. When the measure was local, state_partial was coded 1. On the contrary, when the measure was not localized, state_partial was coded 0. The interaction of state and state_partial allows researchers to create three levels of measures: no state of emergency (state=0 and state_partial =0), partial state of emergency (state=1 and state_partial=1) or national state of emergency (state=1 and state_partial =0); cash: binary variable equal to1 if cash transfers are implemented and 0 otherwise; wage: binary variable equal to1 if wage support is implemented and 0 otherwise; credit: binary variable equal to1 if credit schemes are implemented and 0 otherwise; taxc: binary variable equal to1 if tax credits are implemented and 0 otherwise; taxd: binary variable equal to1 if tax delays are implemented and 0 otherwise; export: binary variable equal to1 if supports to importers or exporters are implemented and 0 otherwise; rate: binary variable equal to1 if the Central Bank lowered the interest rates and 0 otherwise; Rigidity_Public_Health: average of the ten coded public health measures. Public health measures are valued 0.5 if they are localized or partial and 1 if they are national or strict. 0 indicates no measures; Economic_Measures: average of the coded economic measures. The Response2covid19 dataset tracks governments��� responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures ��� 13 public health measures and 7 economic measures ��� taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset (���Governments��� Responses to COVID-19 (Response2covid19)���) and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. Public health measures include international and domestic travel restrictions, bans on mass gatherings, school closing and domestic lockdown among others. Economic measures include wage support, cash transfers, interest rates cuts, tax cuts and delays, and support to exporters or importers. . Smallest Geographic Unit: Country-level other; Cite data as Porcher, Simon "A novel dataset of governments��� responses to COVID-19 all around the world", chaire EPPP discussions paper 2020-03. Link: https://www.chaire-eppp.org/wp-content/uploads/2020/05/WP202003.pdf All countries with available information (228 countries). Response Rates: 62,700 observations; 228 countries.