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8,020 results for “pilots”
Breath-focused mindfulness and compassion training in parent-child dyads: a pilot intervention study
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TagSeq for gene expression in non-model plants: a pilot study at the Santa Rita Experimental Range NEON core site
<p>TagSeq analysis scripts and assembled transcriptomes for four vascular plant species from the Santa Rita Experimental Range, AZ. Transcriptomes for each species were sequenced and assembled as described below. Additional details available in the associated manuscript: MS LINK. Raw reads for each available at NCBI BioProject #PRJNA599443.</p> <p> </p> <p><strong>Taxon selection and sampling </strong></p> <p>This study focused on four commonly-occurring species at the Santa Rita Experimental Range Long Term Research and Core NEON site (SRER). These include the native species <em>Tidestromia</em> <em>lanuginosa</em> (Nutt.) Standl. (Amaranthaceae; ‘woolly tidestromia’), <em>Parkinsonia</em> <em>florida</em> (Benth. ex A. Gray) S. Watson. (Fabaceae; ‘blue palo verde’), and <em>Bouteloua</em> <em>aristidoides</em> (Kunth) Griseb. (Poaceae; ‘needle grama’), as well as the introduced species <em>Eragrostis</em> <em>lehmanniana</em> Nees (Poaceae; ‘Lehmann lovegrass’; native to southern Africa). All species were identified using a combination of the historical flora of the Santa Rita Experimental Range (Medina, 2003), the Arizona Flora (Kearney et al., 1960), and the Flora of North America (Flora of North America Editorial Committee, eds. 1993). Vouchers were deposited in the University of Arizona herbarium (ARIZ). Tissue from mature plants was collected from an apparently healthy individual representing each target species during the 2017 growing season. An entire stem was sampled for <em>B. aristidoides</em> (with flowers and fruits) and <em>E. lehmanniana</em> (without flowers or fruits). Leaves and leaflets only were sampled for <em>P. florida</em> and <em>T. lanuginosa</em>.</p> <p> </p> <p><strong>RNA extraction and RNA-seq</strong></p> <p>Total RNA was extracted from tissue using the Spectrum Plant Total RNA Kit (Sigma-Aldrich Co., St. Louis, MO, USA) following Protocol A. RNA was used to prepare cDNA using Nugen’s Ovation RNA-Seq System via single primer isothermal amplification (Catalogue # 7102-A01) and automated on the Apollo 324 liquid handler (Wafergen). cDNA was quantified on the Nanodrop (Thermo Fisher Scientific) and was sheared to approximately 300 bp fragments using the Covaris M220 ultrasonicator. Libraries were generated using Kapa Biosystem’s library preparation kit (KK8201). Fragments were end repaired and A-tailed, and individual indexes and adapters (Bioo, catalogue #520999) were ligated on each separate sample. The adapter ligated molecules were cleaned using AMPure beads (Agencourt Bioscience/Beckman Coulter, A63883), and amplified with Kapa’s HIFI enzyme (KK2502). Each library was then analyzed for fragment size on an Agilent’s Tapestation, and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on Thermo Fisher Scientific’s Quantstudio 5 before multiplex pooling (13-16 samples per lane) and paired-end sequencing at 2x150 bp on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using fastQC (Andrews, 2010).</p> <p> </p> <p><strong><em>De novo</em> transcriptome assembly</strong></p> <p>Raw sequence reads were processed using the SnoWhite pipeline (Barker et al., 2010a; Dlugosch et al., 2013), which included trimming adapter sequences and bases with a quality score below 20 from the 3' ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust (Lassmann et al., 2009), and removing polyA/T tails with SeqClean (https://sourceforge.net/projects/seqclean/). All transcriptomes were assembled with SOAPdenovo-Trans v1.03 (Xie et al., 2014) using a k-mer of 57. Assembled sequences for each species are in the files ending ".scafSeq".</p> <p> </p> <p><strong>Protein Translations</strong></p> <p>We used TransPipe (Barker et al., 2010) to identify plant proteins within the assembled transcripts for each reference transcriptome and provide protein and in-frame nucleic acid sequences for each species. The reading frame and protein translation for each sequence was identified by comparison to protein sequences from 25 sequenced and annotated plant genomes from Phytozome (Goodstein et al., 2012). Using BLASTX (Wheeler et al., 2008), best hit proteins were paired with each gene at a minimum cutoff of 30% sequence similarity over at least 150 sites. Genes that did not have a best hit protein at this level were removed. To determine the reading frame and generate estimated amino acid sequences, each gene was aligned against its best hit protein by Genewise 2.2.2 (Birney et al., 2004). Based on the highest scoring Genewise DNA-protein alignments, stop and 'N' containing codons were removed to produce estimated amino acid sequences for each gene. Output included paired DNA and protein sequences with the DNA sequence reading frame corresponding to each protein sequence. Nucleic acid sequence files end in “.fna”, whereas amino acid sequence files end in “.faa”. Numbers of sequences in each of these files correspond to the position of the sequence in the associated assembly file.</p> <p> </p> <p><strong>Custom scripts</strong></p> <p>“removePCRdups57.pl” is a Perl script that takes an input FASTQ file and removes exact duplicates identified over a supplied length at the beginning (3’ end) of the read. </p> <p>Run: perl removePCRdups57.pl <inputFASTQ> <length></p> <p> </p> <p>“create_GTF.pl” is a Perl script that takes an input FASTA file and creates a GTF file suitable for input into HtSeq-count v.0.5.4 (Anders et al., 2015).</p> <p>Run: perl create_GTF.pl <inputFASTA></p> <p> </p> <p>“combine_HtSeq.pl” is a Perl script that takes a set of htseq output files and makes a tab delim table of counts with header of sample names and first col of row names. The input file list file should be a text file with lists of Htseq files to combine on each line, where lines are tab delimited of the following form:</p> <p> <NameForOutputFile> <firstHtseqFile> <NextHtseqFile> <...etc...></p> <p>Run: perl combine_HtSeq.pl <inputFileList></p> <p> </p>
SARS-CoV-2 detection dogs - a pilot study
<p>The outstanding olfactory acuity of canines led us to consider whether dogs are able to reliably detect the odour of respiratory diseases associated with a SARS-CoV-2 infection in saliva or tracheobronchial secretion of hospitalized COVID-19 patients. Furthermore, we examined if SARS-CoV-2 detection dogs could provide an appropriate screening method for the human virus.The aim of this data publication is to provide the data acquired in the controlled, randomized and double-blinded pilot study `Scent dog identification of SARS-CoV-2 infection’ (submitted to BMC Infectious Diseases).</p>
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
Data set of trials perfomed in Pont Saint Martin Data Center, pilot of CATALYST Project
<blockquote> <p>The zip file contains data sets being the input and/or output of the test case exploited in Pont Saint Martin Data Center, as pilot of CATALYST Project (H2020-EE-2016-2017)</p> </blockquote>
Pilot implementation, monitoring, co-evaluation and validation data_v1
<p>These data have been collected in the context of monitoring and assessing the pilot operation of the INVITE project (H2020 GA 763651) and its OI2lab platform during their 1st deployment round.</p>
Challenges identified during inDemand in the pilot regions
<p>inDemand is a new co-creation model where Healthcare organizations (Challengers) and companies (Solvers) co-develop Digital Health solutions, with the economic support of public regional funds managed by Regional funding organisations (Funders). inDemand targets to solve Challenges identified by the Healthcare organizations.</p> <p>The first phase focuses on the need identification by the Challenger (Healthcare organisation), who launches an internal call for challenges (i.e.needs) among the professionals in the organisation.</p> <p>During the inDemand project challenges were identified in three pilot regions (Murcia, Oulu and Paris) in two different iterations:</p> <ul> <li>Paris 1<sup>st</sup> call: 05/01/2018 to 31/01/2018</li> <li>Oulu 1<sup>st</sup> call: 09/01/2018 to 28/01/2018</li> <li>Murcia 1<sup>st</sup> call: 06/11/2017 to 20/11/2017</li> <li>Paris 2<sup>nd</sup> call: 15/11/2018 to 31/12/2018</li> <li>Oulu 2<sup>nd</sup> call: 20/09/2018 to 06/10/2018</li> <li>Murcia 2<sup>nd</sup> call: 15/10/2018 to 09/11/2018</li> </ul> <p>In prevision of the Call for Challenges, a questionnaire (common for the 3 pilot regions) was created to collect the most important information necessary to assess and select the challenges in each region:</p> <ul> <li>Responsible of the challenge (name, position, department, email, etc.): anonymised for publication in Zenodo.</li> <li>Title and acronym of the challenge</li> <li>Definition of the challenge (description of the problem, overview of the current way or working, etc.)</li> <li>Potential scalability of the challenge to other areas/hospitals</li> <li>Expected functional requirements of an ICT solution that could tackle the need</li> <li>Feasibility to implement ICT solutions to address the challenge</li> <li>The commitment of the team to be involved in the inDemand model</li> </ul> <p>The data collected was of a qualitative nature. It was received via electronic tool and downloaded into .docx and .pdf formats. </p>
Dataset for pilot evaluation of an enzymatic assay for rapid measurement of antiretroviral drug concentrations
<p>Liquid chromatography tandem mass spectrometry (LC-MS/MS) data, and REverSe TRanscrIptase Chain Termination (RESTRICT) assay data for pilot evaluation study of enzymatic RESTRICT assay for rapid measurement of antiretroviral drug concentrations among people receiving oral pre-exposure prophylaxis (PrEP).</p>
Vr-Together pilot 3: Sound and SFX
<p>VR-Together Pilot 3 sound files and SFX</p> <p>Sound files and SFX for the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>.</p> <ul> <li>Number of files: 23</li> <li>Audio: Stereo, 44,1kHz</li> <li>Bits: 16</li> </ul> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
VR-Together pilot 3: Video Composition (scene 4)
<p>VR-Together Pilot 3 video composition: scene 4.</p> <p>Video composition of the fourth and last scene of the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>.</p> <ul> <li>Dimensions: 3840x2160</li> <li>Codec: Timecode, Linear PCM, H.264</li> <li>Duration: 01:20</li> <li>Audio: Stereo</li> </ul> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
VR-Together pilot 3: Video Composition (scene 1)
<p>VR-Together Pilot 3 video composition: scene 1.</p> <p>Video composition of the first scene of the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>.</p> <ul> <li>Dimensions: 3840x2160</li> <li>Codec: Timecode, Linear PCM, H.264</li> <li>Duration: 04:51</li> <li>Audio: Stereo</li> </ul> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
VR-Together pilot 3: Video Composition (scene 3)
<p>VR-Together Pilot 3 video composition: scene 3.</p> <p>Video composition of the third scene of the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>.</p> <ul> <li>Dimensions: 3840x2160</li> <li>Codec: Timecode, Linear PCM, H.264</li> <li>Duration: 01:49</li> <li>Audio: Stereo</li> </ul> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
Food Chain pilot raw data - Farm, Transportation, Warehouse
<p>Daily GDD (Growing Degree-day) data collected by a weather station involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Handover, temperature, presence data collected in a transportation vehicle (farm to warehouse, warehouse to supermarket) involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p> <p>Temperature, humidity data collected in a warehouse involved in Food Chain Pilot in SOFIE EU project (GA n.779984).</p>
Dataset: Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest
<p>These datasets contain data from (i) soil CO<sub>2</sub>, CH<sub>4,</sub> and N<sub>2</sub>O flux measurements (ii) physicochemical soil properties, and (iii) soil temperature and moisture of four tropical forests located on the western flanks of the Andes in northern Ecuador.</p> <p>A manuscript using these datasets has been submitted to Biogeosciences under the title: <strong><em>"Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest"</em></strong>.</p>
GUT MICROBIOME COMPOSITION IN ALZHEIMER'S DEMENTIA PATIENTS: REPORT FROM PILOT STUDY IN KAZAKHSTAN
<p>Sequencing raw data of microbiome in fastq format</p>
Badge of a pilot of the Naval Air Squadron
ID no.: MLP 914 Museum: Polish Aviation Museum http://muzea.malopolska.pl/en/obiekty/-/a/26873/1124331 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
AllScale : Data for FINE/Open pilot application
<p>You'll find Two meshes:</p> <ul> <li>A Structured one with 10K cells.</li> <li>An Unstructured one with 1000K cells.</li> </ul> <p> </p> <p>For each mesh 4 configuration files:</p> <ul> <li>Steady / Euler / Single grid / Central Matrix numerical scheme</li> <li>Steady / Navier Stokes / Multigrid / Central Matrix numerical scheme</li> <li>Steady / Spalart Allmaras / Multigrid / Central Matrix numerical scheme</li> <li>Unsteady / Spalart Allmaras / Multigrid / Central Matrix numerical scheme</li> </ul> <p> </p> <p>To run a computation, from the application folder:</p> <ul> <li>Copy the computation run in computation_NS.run</li> <li>Change the path of the mesh in the computation file. </li> <li>Run ut_fine_open_NS_solver. </li> </ul> <p>You can compare the residuals obtained with the one provided. </p> <p> </p> <p> </p>
Effects of immersive virtual reality with treadmill in subjects with Rett Syndrome: a pilot study
<p>This dataset is related to a group of 9 girls affected by Rett Syndrome that performed three/four sessions of virtual reality based activity by means of the GRAIL system (Motek).</p><p>Participants underwent an initial assessment of the cognitive and motor aspects using the Modified Raven matrix, the Rett Assessment Rating Scale (RARS) and the Rett Syndrome Gross Motor Scale (GMS). These baseline assessments were performed by an experienced therapist prior to the access to the GRAIL.</p><p>The project consisted of 4 sessions of GRAIL activities in 1 week. During the first 3 sessions, exergames and augmented feedback activities were proposed. During the fourth session a gait analysis was conducted for the subjects with a suitable motor and behavioral level .</p><p>During all the sessions, a video recording was conducted to detect facial expression of the subjects, in order to use this data to compute the "happiness index". The exergames were administered until the therapists detected any sign of discomfort by the subject.</p><p>The outcome of the training, reported in the database were:</p><ul><li>happiness index</li><li>endurance time</li><li>performance speed</li><li>attention focus on scenario</li><li>suitability evaluation questionnaire</li><li>multistep gait analysis with the GRAIL (only 4 patients).</li></ul><p>The Happiness Index and the Suitability Evaluation Questionnaire investigated the feasibility and tolerability of the training with positive results. Additionally, an increase in walking speed, endurance time and focus attention was detected. When possible, subjects underwent a gait assessment whose results provide insights, although preliminary, concerning differences in gait pattern amongst the recruited subjects. Despite the small sample size, the dataset suggests new strategies for Rett rehabilitation.</p><p> </p>
Research data on pass-by sound emission measurements from train units for D7.4-Susteren pilot
<p>This dataset compiles sample measurement results for the N-RSD rail pilot from D7.4-Susteren pilot project report available once approved by the EC at https://cordis.europa.eu/project/id/860441/results. The data includes train unit subtypes, pass-by sound levels, speed, weight, wheel flat indication, brake type information and a sound emission rating. </p>
Universal Mental Health Training Pilot Trial in Ukraine
<p><strong><span>General information</span></strong></p> <p><span>The UMHT is a specialised program developed to train frontline professionals on high-quality and evidence-based responses to the mental health needs of the population they serve. Police officers, emergency responders, social services workers, educators, pharmacists, priests, and other professionals daily interact with a substantial number of people. Whereas their professional roles imply working with people in crisis who experience strong emotions and require support, a high level of mental health awareness and skills to manage mental health issues are needed. Therefore, UMHT was developed as an educational instrument for Ukrainian frontline professionals to raise their mental health awareness, reduce stigma toward people with mental disorders and develop particular skills for giving support.</span></p> <p><span>The training is called Universal because its 5-step model offers a standard frame for interaction with people with mental health issues. Also, it is Universal because it is suitable for different types of frontline workers – the general interaction structure is not changing, only the set of relevant mental health conditions. </span></p> <p><span>The Mental Health Training for Frontline Professionals (UMHT) was developed in 2021 and piloted in 2021-2023 within the context of the Mental Health for Ukraine Project (MH4U), implemented in Ukraine by GFA Consulting Group GmbH (donor - Swiss Confederation). The University of Luxembourg, with the support of the European Commission through the MSCA4Ukraine fellowship scheme by the Alexander von Humboldt Foundation (AvH) for premier investigator Viktoriia Gorbunova, is leading a full-scale efficacy study of the UMHT in 2023-2025. </span></p> <p><strong><span>Data and file overview</span></strong></p> <p><span>Three efficacy measurements were used in the outcome assessment: <em>readiness to interact</em> <em>with people with mental health issues at work</em>,<em> mental health awareness</em>,<em> </em>and <em>mental health proficiency.</em></span></p> <p><em><span>Readiness to interact</span></em><span> <em>with people with mental health issues at work</em></span></p> <p><span>To measure the changes in readiness to interact with people with mental health issues at work (according to the 5-step model), all participants self-assessed their general readiness as well as readiness to do particular actions according to the 5-step model on a five-point scale (from 5 - "absolutely ready" to 1 - "absolutely not ready"). </span></p> <p><span>In the instruction, participants were asked: <em>"Reading the next statements, please assess your readiness for a different kind of interaction with people with mental health conditions. The scale is from 1 to 5, where 1 is the absolute absence of readiness, and 5 – is the absolute readiness".</em> </span></p> <p><span>The next set of statements was proposed to participants:</span></p> <ul> <li><em><span>Readiness to interact with people with mental health issues at work</span></em><span> (general readiness).</span></li> <li><em><span>Readiness to recognise mental health conditions</span></em><span> (readiness for step 1 of the 5-step model).</span></li> <li><em><span>Readiness to initiate and lead conversation with a person with mental health issues and his/her caregivers </span></em><span>(readiness for step 2).</span></li> <li><em><span>Readiness to support a person with mental health issues and his/her caregivers</span></em><span> (readiness for step 3).</span></li> <li><em><span>Readiness to refer a person with mental health issues, and his/her caregivers, to professional support</span></em><span> (readiness for step 4).</span></li> <li><em><span>Readiness to ensure that professional help is received by a person with mental health issues and his/her caregivers</span></em><span> (readiness for step 5).</span></li> </ul> <p><em><span>Mental health awareness</span></em></p> <p><span>Mental health awareness assessment was based on the KAP (knowledge, attitudes, and practices) model <em>(Andrade et al., 2020). </em>There is the experience of using such KAP-based surveys in Ukraine <em>(Quirke et al., 2021).</em> Based on the KAP model, a short survey was developed related to the knowledge about mental health issues, attitudes toward people with mental health disorders, and practice of interaction with them. </span></p> <p><span>Knowledge regarding people with mental disorders was assessed with the query: "Choose the statements that apply to people with mental health disorders" (max = 8 scores, where each score was awarded either for a choice of a correct statement or for a non-selection of a wrong statement):</span></p> <ul> <li><em><span>They are dangerous to the people around them.</span></em></li> <li><em><span>They are themselves guilty of their condition.</span></em></li> <li><em><span>They are incapable of true friendships.</span></em></li> <li><em><span>They can work.</span></em></li> <li><em><span>By appearance, it is clear that the person is not all right.</span></em></li> <li><em><span>Anyone can have a mental disorder.</span></em></li> <li><em><span>Mental disorders are incurable.</span></em></li> <li><em><span>Most people with mental disorders can recover.</span></em></li> </ul> <p><span>Attitude towards people with mental issues was assessed with the question: <em>"What is the best way of behaviour for people with mental health issues?"</em> (max = 8 scores):</span></p> <ul> <li><em><span>Do not tell anyone about their condition. </span></em></li> <li><em><span>Discuss everything with a doctor, but do not inform relatives.</span></em></li> <li><em><span>Hide this information at work/school.</span></em></li> <li><em><span>Tell loved ones and ask for help from specialists.</span></em></li> <li><em><span>Hide it from the family.</span></em></li> <li><em><span>Live among those like themselves.</span></em></li> <li><em><span>Should not marry and have children.</span></em></li> </ul> <p><span>The question for assessment practices of interactions with people with mental disorders: <em>"What is the proper way of interactions with people with mental health disorders?"</em> (max = 9 scores):</span></p> <ul> <li><em><span>You would better avoid any contact with them.</span></em></li> <li><em><span>You shouldn't allow them to make any decisions.</span></em></li> <li><em><span>You would better avoid working with them in one team or performing tasks together.</span></em></li> <li><em><span>You should be careful about conversations with them.</span></em></li> <li><em><span>You should be ashamed and try to hide the fact you have a relative with a mental health disorder.</span></em></li> <li><em><span>They should have the same rights as anyone else.</span></em></li> <li><em><span>It is normal to have a friend with a mental health disorder.</span></em></li> <li><em><span>It is normal to marry a person with a mental health disorder.</span></em></li> <li><em><span>You should treat them with care and sympathy.</span></em></li> </ul> <p><span>Practices of care about people with mental health issues were analysed with the question: <em>"What is the best way to care about people with mental health issues?"</em> (max = 6 scores):</span></p> <ul> <li><em><span>In a psychiatric hospital where they are under supervision and control (psychiatrist).</span></em></li> <li><em><span>Outside the hospital in specialised centres or privately (psychologist, psychotherapist).</span></em></li> <li><em><span>Alternative methods of treatment (traditional medicine, homoeopathy, vitamins, massage).</span></em></li> <li><em><span>Normal family relationships is the best treatment.</span></em></li> <li><em><span>Do not waste energy, it is not possible to cure mental disorders.</span></em></li> <li><em><span>At the primary level of health care (family doctor, paediatrician, general practitioner).</span></em></li> </ul> <p><span>Mental health awareness scores were collected as the sum of scores for each scale.</span></p> <p><em><span>Mental health proficiency</span></em></p> <p><span>Mental health proficiency, as the ability to recognise mental health disorders' symptoms, was assessed by the tests that include correct and non-correct symptoms. Three true and two false symptoms (based on DSM-5) were offered for selection in each case. Mental health proficiency was estimated as the sum of the correct choices of symptoms for every disorder learned by participants. For instance, the participants who worked during the training with depressive disorders should choose all appropriate parameters among depressed mood, markedly diminished interest or pleasure in almost all activities, excessive or inappropriate feelings of worthlessness or guilt, inattention as, difficulties following instructions and failure to finish tasks, restlessness as fidgeting with or tapping hands or feet or squirming in the seat.</span></p> <p><em><span>Additional one-month follow-up questions</span></em></p> <p><span>Additional questions for the one-month follow-up test were: "Did you work after the training with people with mental health issues that you studied?", "What kind of the issues?", "Did you use training knowledge and skills?", "Which knowledge and skills did you use in particular?" </span></p> <p><strong><span>Sharing and accessing information</span></strong></p> <p><span>Information (raw anonymized data) is openly available through Zenodo. It is possible to use the information with research aims to evaluate UMHT or compare data with other similar programs. Our research team kindly asks to notify the contact person (Viktoriia Gorbunova) about any usage of the dataset. </span></p> <p><strong><span>Methodological information</span></strong></p> <p><span>The study was quasi-experimental (no complete randomization was possible at this piloting stage). Two groups were involved: the experimental group (received UMHT) and the control group (no training, waiting list).</span></p> <p><span>The pilot trial of UMHTs' efficacy was conducted with 307 frontline professionals divided into 24 training groups (social workers (12 groups, 128 persons), educators (4, 63), police officers (4, 60), priests and clerics (1, 15), military volunteers (1, 12), workers of occupation centres (1, 13), emergency workers (1, 16)). All participants were recruited for training by their team leaders, who were informed about training possibilities by letters sent from the training developers. The only requirement for participation was working in the field with people. </span></p> <p><span>The control group included 211 persons with the same occupation background who participated in training later (waiting list). The control group consisted of social workers (97 persons), educators (32), police officers (40), priests and clerics (12), military volunteers (13), workers of occupation centres (7), and emergency workers (10).</span></p> <p><strong><span>Data-specific information</span></strong></p> <p><span>Excel file (UMHT_dataset_pilot_trial.xlsx) containing four pages. </span></p> <p><span>1. Page "Training groups_before UMHT". Contains the answers to questionaries completed by UMHT training participants before the training. </span></p> <p><span>2. Page "Training groups_after UMHT". Contains the answers to questionaries completed by UMHT training participants immediately after the training. </span></p> <p><span>3. Page "Training groups_after one month". Contains the answers to questionaries completed by UMHT training participants the month before the training. </span></p> <p><span>4. Page "Control group_before-after". Contains the answers to questionaries completed by UMHT control group participants (waiting list) before and after the training.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.