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4,175 results for “university”
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>
A Terrylene Bisimide based Universal Host for Aromatic Guests to Derive Contact Surface-Dependent Dispersion Energies
<p>Additional data to report <a href="https://doi.org/10.1002/anie.202318451">https://doi.org/10.1002/anie.202318451</a>:<br><br>π–π interactions are among the most important intermolecular interactions in supramolecular systems. Here we determine experimentally a universal parameter for their strength that is simply based on the size of the interacting contact surfaces. Toward this goal we designed a new cyclophane based on terrylene bisimide (TBI) π-walls connected by <em>para</em>-xylylene spacer units. With its extended π-surface this cyclophane proved to be an excellent and universal host for the complexation of π-conjugated guests, including small and large polycyclic aromatic hydrocarbons (PAHs) as well as dye molecules. The observed binding constants range up to 10<sup>8</sup> M<sup>−1</sup> and show a linear dependence on the 2D area size of the guest molecules. This correlation can be used for the prediction of binding constants and for the design of new host–guest systems based on the herewith derived universal Gibbs interaction energy parameter of 0.31 kJ/molÅ<sup>2</sup> in chloroform.</p>
University of New Hampshire Pressure Mapped Munition (PMM) Experiments 2019 - 2021
<p>This project contains the data collected by the University of New Hampshire (UNH) Coastal Processes Lab for three field experiments focusing on investigating munition mobility in nearshore/surfzone regions. The first experiment took place on May 17 2019, the second experiment took place on February 1-2 2021, and the last experiment to place on October 26-27 2021. The data was collected at Wallis Sands Beach in Rye, New Hampshire. </p> <p>The main instrument utilized was the Pressure Mapped Munition (PMM), which is a fully autonomous cylindrical surrogate munition that can resolve the pressure field on its surface. The PMM is constructed from a 229 mm long section of 304 stainless steel pipe with a 140 mm outside diameter and 12.7 mm wall thickness. To make autonomous measurements of surface pressure and relative position, the PMM houses 16 high resolution TE Connectivity MS5837-02BA absolute pressure sensors and a Lord MicroStrain 3DM®-GX5-25 inertial measurement unit (IMU). There are two rings of pressure sensors around the cylinder. Pressure sensors 1-8 are in ring 1 and pressure sensors 9-16 are in ring 2. Each ring is 6.35 cm from the ends of the cylinder and the rings are 15.24 cm apart. The radial spacing between sensors around each ring is 45°, which minimizes the arc length and height difference between sensors while maintaining axial pairs of sensors. This orientation allows for sensor redundancy in the case of sensor failure and allows for error correction if faulty values from one of the sensors is suspected. When the PMM is lying flat the vertical displacement between the topmost and bottommost sensor is 129 - 140 mm depending on the orientation of the instrument. </p> <p>For the May experiment, a Nortek Vector was deployed along with the PMM. The Nortek Vector is an Acoustic Doppler Velocimeter (ADV) which collects high-resolution, single-point, 3-dimensional velocity data (Nortek, 2022b). For this experiment the ADV transducer was about 30 cm above the sediment bed while the ADV pressure sensor was about 50 cm above the sediment bed. The Nortek Vector was not deployed for the Feburary and October experiments.</p> <p>For the February and October experiments, another instrument, the Pressure Stick (PS), was deployed with the PMM. The Pressure Stick is a fully autonomous pressure-profiling instrument. The PS contains eight micro-controlled, time-synced TE Connectivity MS5837-02BA absolute pressure and temperature sensors distributed along a 70 cm distance to measure pressure throughout the water column and into the sediment bed. The Pressure Stick allows for a quantification of intermittent bed instability (momentary liquefaction) and its potential role in munition mobility. More information about the PS can be found in Marry & Foster (2024).</p> <p>Global Positioning System (GPS) surveys of the beach profile were also completed on February 2, 2021 (at the end of the experiment) and October 26, 2021 (at the beginning of the experiment), and the survey data is included in this project. Data of the offshore wave conditions as measured by the Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC) (https://www.ndbc.noaa.gov/station_page.php?station=44098) for all three experiments are given here as well.</p> <p>This effort was supported by Strategic Environmental Research and Development Program (SERDP, Project Number 17 MR-2731). These datasets are presented in the final report for Project Number 17 MR-2731.</p> <h2>Dataset files</h2> <p>A description of each data file is given below:</p> <h3>Pressure Mapped Munition (PMM) Data</h3> <p>1. PMM_20190517.txt<br> This file contains the raw data from the PMM for the experiment on May 17, 2019<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>2. IMU_20190517.txt<br> This file contains the raw IMU data from the PMM for the experiment on May 17, 2019<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <p>3. PMM_20210202.txt<br> This file contains the raw data from the PMM for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>4. IMU_20210202.txt<br> This file contains the raw IMU data from the PMM for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <p>5. PMM_20211026.txt<br> This file contains the raw data from the PMM for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>6. IMU_20211026.txt<br> This file contains the raw IMU data from the PMM for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <h3>Nortek Vector Data</h3> <p>Vector_20190517.txt<br> This file contains the Nortek Vector data from the experiment on May 17 2019.<br> - Vec_time = time of Vector samples<br> - Vpress = pressure in mbar<br> - Vpress_smooth = smoothed pressure in mbar<br> - Vu = cross-shore velocity in m/s<br> - Vv = along-shore velocity in m/s<br> - Vw = vertical velocity in m/s</p> <h3>Pressure Stick (PS) Data</h3> <p>1. PS_20210202.txt</p> <p> This file contains the raw data from the Pressure Stick for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 25 cm above the sediment bed<br> - P2 = pressure from sensor 2 in mbar, sensor 2 is about 15 cm above the sediment bed<br> - P3 = pressure from sensor 3 in mbar, sensor 3 is about 9 cm above the sediment bed<br> - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm above the sediment bed<br> - P5 = pressure from sensor 5 in mbar, sensor 5 is about 3 cm in the sediment bed<br> - P6 = pressure from sensor 6 in mbar, sensor 6 is about 13 cm in the sediment bed<br> - P7 = pressure from sensor 7 in mbar, sensor 7 is about 28 cm in the sediment bed<br> - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 43 cm in the sediment bed<br> - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 25 cm above the sediment bed<br> - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 15 cm above the sediment bed<br> - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 9 cm above the sediment bed<br> - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm above the sediment bed<br> - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 3 cm in the sediment bed<br> - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 13 cm in the sediment bed<br> - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 28 cm in the sediment bed<br> - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 43 cm in the sediment bed </p> <p>2. PS_20211026.txt</p> <p> This file contains the raw data from the Pressure Stick for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 19 cm above the sediment bed<br> - P2 = pressure from sensor 2 in mbar, sensor 2 is about 9 cm above the sediment bed<br> - P3 = pressure from sensor 3 in mbar, sensor 3 is about 3 cm above the sediment bed<br> - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm in the sediment bed<br> - P5 = pressure from sensor 5 in mbar, sensor 5 is about 9 cm in the sediment bed<br> - P6 = pressure from sensor 6 in mbar, sensor 6 is about 19 cm in the sediment bed<br> - P7 = pressure from sensor 7 in mbar, sensor 7 is about 34 cm in the sediment bed<br> - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 49 cm in the sediment bed<br> - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 19 cm above the sediment bed<br> - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 9 cm above the sediment bed<br> - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 3 cm above the sediment bed<br> - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm in the sediment bed<br> - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 9 cm in the sediment bed<br> - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 19 cm in the sediment bed<br> - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 34 cm in the sediment bed<br> - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 49 cm in the sediment bed </p> <h3>GPS Data</h3> <p>1. UNH_GPS_WS_20210202.txt (.pos)</p> <p> This file contains data from a GPS survey taken on February 2nd 2021 as the post-deployment survey for the February experiment. <br> - GPST = time stamp of the survey sample <br> - latitude = latitude in degrees<br> - longitude = longitude in degrees<br> - height = elevation height, relative to WGS84/ellipsoidal in meters<br> - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points. <br> - ns = Number of satellites<br> <br>2. UNH_GPS_WS_20211026.txt (.pos)</p> <p> This file contains data from a GPS survey taken on October 26th 2021 as the pre-deployment survey for the October experiment. <br> - GPST = time stamp of the survey sample <br> - latitude = latitude in degrees<br> - longitude = longitude in degrees<br> - height = elevation height, relative to WGS84/ellipsoidal in meters<br> - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points. <br> - ns = Number of satellites</p> <h3>Waverider Buoy (NDBC station 44098) Data</h3> <p>1. NDBC_44098_JeffreysLedgeBuoy_May2019.txt</p> <p> This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the May 2019 experiment (May 17th 2019 00:08:00 - May 18th 2019 12:38:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p> <p>2. NDBC_44098_JeffreysLedgeBuoy_February2021.txt</p> <p> This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the February 2021 experiment (February 1st 2021 01:26:00 - Feburary 2nd 2021 09:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p> <p>3. NDBC_44098_JeffreysLedgeBuoy_October2021.txt</p> <p> This file contains offshore wave data from [Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)](https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the October 2021 experiment (October 26th 2021 00:26:00 - October 27th 2021 23:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p>
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
Investigating the universality of five-point QCD scattering amplitudes at high energy
<p>We provide various analytic results for one- and two-loop five-point QCD scattering amplitudes in multi-Regge kinematics (MRK).<br>If you use the results distributed with this repository in your research work, please cite <a href="https://arxiv.org/abs/2411.14050">2411.14050</a>.<br><br>This repository contains two archives:</p> <ol> <li><strong>mrk_results.tar.gz</strong>: all the analytic results in Mathematica readable format are collected here. For a detailed description of their content and the notation adopted see README file in this archive.<br><br></li> <li><strong>expansions_n4lp.tar.gz</strong>: in this archive the expansions of the one- and two-loop massless pentagon functions up to N^4LP are provided (see section 2 of the <a href="https://arxiv.org/abs/2411.14050">paper</a> for a detailed description of the beyond leading-power expansion). Different expansions are performed in the upper and lower z-complex plane (see section of 2 of the <a href="https://arxiv.org/abs/2411.14050">paper</a>). It further contains a Mathematica script, README.wl, which serves both as a description and example file.</li> </ol>
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
Pathogen-sugar interactions revealed by universal saturation transfer analysis
<p>Supporting data for the "Pathogen-sugar interactions revealed by universal saturation transfer analysis" manuscript.</p>
Universal metabolic model for Fungi
<p>Universal metabolic model for Fungi. It is a combination of enzymatic reactions collected from literature and reaction databases (Kegg, metacyc, Rhea).</p>
TRENDS AND PATTERNS OF RESEARCH PUBLICATIONS AT KYAMBOGO UNIVERSITY BETWEEN 2003 TO 2020
<p>Data for the study was sourced from: Google Scholar, Emerald, Ebscohost, Taylor and Francis and Kyambogo University Scholars’ Space. This took place between June 2019 and June 2020. Data sourced was based on two specific criteria: having acknowledged to being a staff member of the University and having published within the period 2003 to 2020.</p>
Valencia Archaeological Surveys (1990-2004), Arizona State University and University of Valencia
<p>Archaeological survey data from Valencia, Spain</p>
The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at z~8
<p>Reduced and flux calibrated JWST/NIRSpec 1D spectra for the three sources (ID_4590 at z=8.4953, ID_6355 at z=7.6643 and ID_10612 at z=7.6592) analysed in Curti et al., 2022, "The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at 𝑧~8" (published on MNRAS, Volume 518, Issue 1, pp.425-438)</p> <p>For more details on the data processing we refer to the Section 2.1 of the paper.</p> <p> </p> <p> </p>
Bibliographic metadata of articles in "Krankenpflege" and "GERONTOLOGIE CH. Praxis + Forschung" published by Swiss university members
<p>The swissuniversities-funded project GOAL (Unlocking the Green Open Access PotentiaL in scholarly and professional journals in Switzerland) aims to develop case scenarios for the semi-automatic inclusion of full-text articles from professional journals in Open Access repositories. These articles originate from journals with which the project has successfully negotiated self-archiving rights. To this end, the project is testing semi-automated workflows that process and enrich bibliographic metadata and full-texts provided by two professional journals. The dataset contains bibliographic metadata of articles published in the two professional journals “Krankenpflege” (ISSN: 0253-0465) and “GERONTOLOGIE CH. Praxis + Forschung” by members of Swiss higher education institutions (HEI). Metadata provided by the publisher itself (Gerontologie CH.) or the database CINAHL Ultimate (“Krankenpflege”) were obtained, enriched, partly checked, and corrected to create a list of articles in tabular form as Excel and CSV of all articles published by Swiss university members between 2020 and 2023. Additionally, a table with the names of all swissuniversities members was created as a reference point to normalize the affiliation data. </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>
Dataset and additional figures for: decomposing geographical and universal aspects of human mobility
<p>Data and additional figures for: <em>Decomposing geographical and universal aspects of human mobility, </em><a href="https://arxiv.org/pdf/2405.08746">https://arxiv.org/pdf/2405.08746</a></p>
2023 Utrecht University Open Access Monitor (peer reviewed journal articles)
<p>Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2023. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht. </p>
Loflòc: A Morphological Lexicon for Occitan using Universal Dependencies
<p><strong>LOFLOC -- Lexic obèrt flechit Occitan (Open Inflected Lexicon of Occitan)</strong></p> <p>Loflòc is a morphological lexicon for Occitan, a Romance language spoken in the south of France and in parts of Italy and Spain. Occitan is not recognized as an official language in France and no standard variety is shared across the linguistic area. To the best of our knowledge, Loflòc is the first publicly available lexicon for Occitan. It contains 680 thousand entries for 57 thousand lemmas. Each entry contains an inflected form, its lemma and its part-of-speech tag according to the Universal Dependencies guidelines. Currently, the lexicon only contains the Lengadocian variety and the classical spelling norm. Nevertheless, it has been shown to be useful even for processing texts from other varieties (for more details, see Vergez-Couret et al., 2024; full reference below).</p>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
University dropout: A systematic review of the main determinant factors
<p><strong><span>Introduction:</span></strong><span> This research is a systematic review aimed at synthesizing scientific evidence on the causes of university dropout, focusing on the subcategories of vocational guidance, academic performance, socioeconomic status, and institutional aspects between 2020 and June 2024. <strong>Methods:</strong> Only articles addressing university dropout were considered, analyzing dimensions such as vocational guidance, academic performance, socioeconomic status, and institutional aspects. Articles published in indexed scientific journals with double-blind, double-blind peer, or open reviews between 2020 and June 2024 were included. The main databases used were Scopus, Web of Science, and Google Scholar. To assess the risk of bias in qualitative studies, the criteria from the article "Validity criteria for qualitative research: three epistemological strands for the same purpose" were used. For quantitative studies, the criteria from the article "Evaluating survey research in articles published in Library Science journals" were followed. For mixed-method studies, both sets of criteria were combined. <strong>Results:</strong> A total of 23 studies were included: 15 quantitative (65.22%), 3 qualitative (13.04%), and 5 mixed-method (21.74%). All studies (100%) addressed the subcategories of socioeconomic status and institutional aspects. Regarding the academic performance subcategory, 86% of the studies addressed it, while the vocational guidance subcategory was covered by 73.91% of the studies. <strong>Conclusions:</strong> Vocational guidance, academic performance, socioeconomic status, and institutional aspects are crucial for reducing university dropout. Providing adequate professional guidance, academic support, financial assistance, and strong institutional support is fundamental to improving student retention and academic success.</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.