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Consensus-seeking and conflict-resolving: an fMRI study on college couples’ shopping interaction
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Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
NC Community College President Data Set
<p>NCCCPDS. A working data set of presidents serving in North Carolina community colleges from the mid-1960s. Includes name, year, college, gender identity, degree, degree university, and field. Data were retrieved from publicly available documents including course catalogs, newspapers, obituaries, and university alumni records.</p>
Horizon Europe Cluster 2 Award - Case Study - Prof Kath Browne, University College Dublin
<p>Video features Prof Kath Browne, PI on the RESIST Project Team, who share thier refleciton on working on the project funded under the EC Horizon Europe, Pillar 2, Cluster 2 “Culture, Creativity and Inclusive Society”.</p> <p>Video is avialbe on the YouTube channels of:</p> <ul> <li>Irish Marie Skłodowska-Curie Office <a href="https://youtu.be/-yWMLGxVz0w?feature=shared" target="_blank" rel="noopener">https://youtu.be/-yWMLGxVz0w?feature=shared</a> </li> <li>RESIST Project Videos <a href="https://www.youtube.com/@resistproject/playlists" target="_blank" rel="noopener">https://www.youtube.com/@resistproject/playlists</a></li> </ul>
Diachronic Corpus of Mission Statements for NC and FL Community Colleges
<p>This is a diachronic corpus of mission statements, philosophy statements, and purpose statements for community colleges in North Carolina and Florida. Texts date from the mid-1960s to 2020. Texts are indexed to IPEDS unit id. Texts for some years are missing. "OTM" means other than mission (which is typically a statement of purpose but may include statement of goals). Data were retrieved from archived catalogs and archived websites (e.g., Wayback Machine). The highest level of heading was used. For example, if a college published a statement of mission and a statement of purpose, the statement with the most prominent (typically the first) heading was used. </p>
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
Arthropod communities in the Second College Grant, NH
Arthropods are active during the winter in temperate regions. Many use the seasonal snowpack as a buffer against harsh ambient conditions and remain active in a refugium known as the subnivium. While the use of the subnivium by insects and other arthropods is well-established, far less is known about winter community composition, abundance, biomass, and diversity and how these characteristics compare with the community in the summer. Understanding subnivean communities is especially important given observed and anticipated changes in snowpack depth and duration with changing climate. We studied winter and summer insects and other arthropods using pitfall trapping in northern New Hampshire, where snowpack is still relatively intact. We found that compositions of the subnivium and summer arthropod communities differed. The subnivium arthropod community featured moderate levels of richness and other measures of diversity that tended to be lower than in the summer community. More striking, the subnivium community was much lower in overall abundance and biomass than the summer community. Interestingly, some groups and species of arthropods were dominant in the subnivium but either rare or absent in summer collections. These putative “subnivium specialists” included one spider (order: Araneae), Cicurina brevis (Emerton, 1890), and three rove beetles (order: Coleoptera, family: Staphylinidae) Arpedium cribratum Fauvel, 1878, Lesteva pallipes LeConte, 1863, and Porrhodites inflatus (Hatch, 1957). This study provides a detailed account of the subnivium arthropod community, presents novel concepts, and establishes baseline information on arthropod communities in the North American northeastern temperate forest.
Bibliographic data and analysis of COVID-19 research outputs from Imperial College London 16.01.2020-02.04.2020
<p>Bibliographic data and analysis of 41 research outputs, including reports/preprints/published articles/code, identified as having Imperial authorship and being relevant to COVID-19, published between 16.01.2020 - 02.04.2020. </p> <p>Related report can be found at: Price RC and Ozkan YA. 13 weeks in a pandemic: a descriptive study of Imperial College London’s COVID-19 publications. Imperial College London (April 2020), https://doi.org/10.25561/77970</p>
Imperial College London femur and tibia surface mesh set
<p>The dataset contains bone geometries of the left and right thigh (femur) and shank (tibia and fibula) segmented from magnetic resonance (MR) scans of 35 healthy volunteers (22 male, 13 female; height 155 cm to 193 cm, weight 45 kg to 108 kg, body mass index 17.0 kg/m<sup>2 </sup>to 34.8 kg/m<sup>2</sup>).</p> <p>If you are using this dataset, please cite:</p> <p>Nolte, D., Tsang, C.K., Zhang, K., Ding, Z., Kedgley, A.E., Bull, A.M.J., 2016. Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models. J. Biomech. 49, 3576–3581. doi:10.1016/j.jbiomech.2016.09.005</p>
In-situ Treatment of manuscripts and Printed Books in Trinity College Dublin
<p>This is the recording and transcript of a lecture given by Anthony Cains in 1988 at the Fifth Anniversary Conference of the Parker Library Conservation Project. A publication based upon the conference paper, supplemented with new material and updated, was released in 1994:</p> <p>Cains, Anthony G. 1994. ‘<em>In-Situ</em> Treatment of Manuscripts and Printed Books in Trinity College Dublin’. In <em>Conservation and Preservation in Small Libraries</em>, edited by Nicholas Hadgraft and Katherine Swift, 127–31. Cambridge: Parker Library Publications.</p>
Research Data Stewardship Survey - University College Cork
<p>This survey aimed to help us gain an understanding of research data stewardship activities in UCC, the scope of those activities, identify any gaps in current resources and skills and work out where the Research Data Service fits with related roles and services. We hoped this activity would also help with the development of a data stewardship network across UCC for support, skills sharing, peer learning and the development of tailored skills development programs within UCC. It would also provide an evidence base to inform the model UCC should adopt in meeting its future research data requirements.</p> <p><br>Funders and publishers increasingly require researchers to formally manage their data and encourage or mandate FAIR and/or Open Data outputs. Both the National and European Codes of Research Conduct recognise that data management is central to research integrity and the quality and trustworthiness of research outputs across all disciplines. Research infrastructures in Europe are currently in a phase of development with continued expansion of the European Open Science Cloud (EOSC) and related<br>services. Successive reports internationally (Realising the EOSC, 2016, Turning FAIR into a Reality, 2018) and our own recently compiled National Landscape Report (NORF, 2021) highlighted a resource and skills gap in meeting the expectations and potential of FAIR research data and related research<br>infrastructures. Specifically, in relation to FAIR and Open Data, a set of skills, competencies, and responsibilities have been identified and grouped together under the umbrella of a new “Research Data Steward” role. Research data stewardship encompasses all the various tasks and responsibilities that<br>relate to research data management throughout the entire research lifecycle. The role of data steward is not universally defined yet and is influenced by the context and the needs of the researcher or unit. Across Europe, Research Performing Organisations have taken concrete steps to address this gap, for example by appointing new data steward positions or by re-focusing existing institutional skills and supports into designated competency centres for research data supports. TU Delft is an exemplar where eight newly established embedded data stewards, with domain expertise in the relevant faculty, complement a similar number of support staff based in central services such as the Library and IT Services.</p> <p><br>In UCC the Research Data Service provides a range of data stewardship supports to the research community from advisory to tailored training. The Research Data Service and Research Data Coordinator work closely with related services and roles to provide holistic advice on research data management to the UCC research community. The Clinical Research Facility–Cork has also developed a data stewardship service which is available on a consultancy basis to funded human focused research projects. However, the ask of researchers in terms of funder mandated data management plans and commitments to FAIR and Open Data continues to increase. Certainly in the case of the Research Data Service full capacity is fast approaching. As funders embed Open Science, and by extension data management, FAIR, and Open Data more firmly in their policies and requirements there is a risk that this will impact the competitiveness of our funding applications and the reach, impact and quality of our research outputs if we cannot meet researchers increasingly complex needs for research data stewardship support.</p> <p>We know that there are those engaged in research data stewardship activities throughout UCC although this may not be reflected in their job title. Those who engage in research data stewardship activities do not always identify as Data Stewards but contribute significantly to the data management lifecycle associated with research projects. Each stage of a research project can have specialist data stewardship requirements - these tasks are performed by people in a range of roles and positions including researchers, project managers, data managers, statisticians and data analysts, research assistants, technicians, systems administrators, or research software engineers to name but a few. To develop a holistic and coordinated approach data stewardship and research data management we needed to hear from the whole research ecosystem, those engaged in research and those facilitating it.</p>
A questionnaire survey of expected characteristics of i-voting systems among students and graduates of agricultural colleges
<p>In order to find out the opinions on the transparency of remote electronic voting, a questionnaire survey was conducted among students and graduates of Czech universities with agricultural specialization. We assume that these are people with higher technical literacy who may be potential users of i-voting systems in farms in the near future. For the composition of the questions, the framework from Agbesi et al. (2023) was used to investigate the dimensions of transparency for Internet voting, supplemented with a few specific questions. Agbesi et al. (2023) identify five core dimensions, namely information accessibility, clarity, monitoring and verifiability, corrective action and testing, these dimensions influence the perception of transparency which in turn influences trust in the overall system. The anonymous questionnaire survey was conducted online via the Dotaznik.czu.cz platform operated by the Czech University of Life Sciences Prague. The invitation to participate in the survey was extended primarily to Czech students and graduates of agricultural universities. The questionnaire survey was conducted from 24 October 2023 to 12 May 2024. The questionnaire was freely accessible on the platform, therefore some respondents may be outside the target population within the limitations of the research. Participants were shown all information including consent to data processing on the homepage of the survey.</p> <p>Respondents answered on a seven-point Likert scale ranging from Strongly Disagree (0) to Strongly Agree (6) to statements within the five defined dimensions. A total of 177 individuals were recorded as completing the questionnaire. A total of 108 questionnaires were completed in full. Of these, 8 more questionnaires were removed because the control question "This question is not part of the survey and just helps us to detect bots and automated scripts. To confirm that you are a human, please choose 'Strongly agree' here" was answered differently than Strongly agree. Of the 100 responses examined, 64 were male, 35 were female, and 1 respondent did not indicate their gender. 72 respondents are aged 18-30, 18 aged 31-40, 5 aged 41-50, 2 aged 51-60 and 3 aged 61-70. 72 respondents have completed secondary education, 10 have a Bachelor's degree, 12 have a Master's degree and 6 have a PhD.</p>
Fig. 2 in The distribution of overwintering brown marmorated stink bugs (Hemiptera: Pentatomidae) in college dormitories
Fig. 2. Percentage of rooms where residents observed BMSB, shown by floor. Data are pooled from Perry and Voorhees residence halls. Bars with the same letter shown above are not statistically different as determined with a Kruskal– Walis test (P> 0.05).
Fig. 1 in The distribution of overwintering brown marmorated stink bugs (Hemiptera: Pentatomidae) in college dormitories
Fig. 1. Floor plans for Perry (lef) and Voorhees (right). Gray and white rooms represent rooms where overwintering BMSB was and was not observed, respectively, by the occupants. Diagonally patterned rooms represent rooms where no data were collected.
Data for "Credit card risk behavior on college campuses: evidence from Brazil"
<p>This data set support the following research: College students frequently show they have little skill when it comes to using a credit card in a responsible manner. This article deals with this issue in an emerging market and in a pioneering manner. University students (<em>n</em> = 769) in São Paulo, Brazil's main financial center, replied to a questionnaire about their credit card use habits. Using Logit models, associations were discovered between personal characteristics and credit card use habits that involve financially risky behavior. The main results were: (a) a larger number of credit cards increases the probability of risky behavior; (b) students who alleged they knew what interest rates the card administrators were charging were less inclined to engage in risky behavior. The results are of interest to the financial industry, to university managers and to policy makers. This article points to the advisability, indeed necessity, of providing students with information about the use of financial products (notably credit cards) bearing in mind the high interest rates which their users are charged. The findings regarding student behavior in the use of credit cards in emerging economies are both significant and relevant. Furthermore, financial literature, while recognizing the importance of the topic, has not significantly examined the phenomenon in emerging economies.</p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
University College Dublin bird observations - 1973 to 1998
<p>This folder holds data that CP entered from ledgers provided by K. Cathcart recording birds on the campus of University College Dublin, Belfield, Co. Dublin, Ireland from 1973 to 1998. Birds were recorded weekly with either presence/absence or abundance codes. These data have not been cleaned, and are known to have errors (e.g. some impossible dates including 31 November 1985 were written in the original ledgers and have been transcribed exactly into this dataset). </p> <p>These data have not been double-checked to identify entry errors. </p>
Gayatri Chakravorty Spivak at Goldsmiths College, University of London, 2007
<p>Gayatri Chakravorty Spivak at Goldsmiths College, University of London, 2007, photography by Shih-Lun Chang.</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.