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143 results for “Intersectionality”
AIM aggregated dataset of normal driving behavior at intersections
<p>As part of the Application Platform for Intelligent Mobility (AIM), the traffic situation of an intersection in Braunschweig (Germany) was recorded in order to better understand the behavior of road users. In the project L3pilot, the normal driving behavior at the intersection was analyzed. We focused on kinematic and interaction behaviour of a vehicle (turning left or right, going straight) with oncoming road users (VRU and motorised vehicles) and with lead vehicle. Altogether, 30 days of trajectory data of different months of 2018 and 2019 of the relevant scenarios were analyzed. The datasets include the aggregated parameters in the following scenarios: </p> <ul> <li>Scenario L1: left turning following vehicle from West to North interacting with the lead vehicle from West to North.</li> <li>Scenario L2: left turning vehicle from West to North interacting with oncoming vehicle from East to West.</li> <li>Scenario L3: left turning vehicle from West to North interacting with oncoming bicycle.</li> <li>Scenario R1: right turning following vehicle from East to North interacting with the lead vehicle from East to North.</li> <li>Scenario R2: right turning vehicle from East to North interacting with bicycle from East to West.</li> <li>Scenario S: straight driving following vehicle from East to West interacting with the lead vehicle from East to West.</li> </ul> <p> </p> <table> <caption>meta table of car following</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>the following vehicle stopped (stop) or not (non-stop)</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>m(d)</td> <td>m</td> <td>average distance to lead vehicle</td> </tr> <tr> <td>14</td> <td>min(d)</td> <td>m</td> <td>minimum distance to lead vehicle</td> </tr> <tr> <td>15</td> <td>m(THW)</td> <td>s</td> <td>average time headway</td> </tr> <tr> <td>16</td> <td>min(THW)</td> <td>s</td> <td>minimum time headway</td> </tr> <tr> <td>17</td> <td>m(TTC)</td> <td>s</td> <td>average time to collision</td> </tr> <tr> <td>18</td> <td>min(TTC)</td> <td>s</td> <td>minimum time to collision</td> </tr> <tr> <td>19</td> <td>v_min(TTC)</td> <td>m/s</td> <td>velocity of following vehicle at the minimum time to collision</td> </tr> <tr> <td>20</td> <td>a_min(TTC)</td> <td>m/s²</td> <td>acceleration of following vehicle at the minimum time to collision</td> </tr> <tr> <td>21</td> <td>THW_min(TTC)</td> <td>s</td> <td>time headway at the minimum time to collision</td> </tr> <tr> <td>22</td> <td>d_min(TTC)</td> <td>m</td> <td>distance to lead vehicle at the minimum time to collision</td> </tr> </tbody> </table> <p> </p> <table> <caption>meta table of crossing</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>vehicle yielded (yielding) or didn't yield (non-yielding) to the oncoming road user</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>PET</td> <td>s</td> <td>post encroachment time</td> </tr> <tr> <td>14</td> <td>m(TAdv)</td> <td>s</td> <td>average time advantage</td> </tr> <tr> <td>15</td> <td>min(TAdv)</td> <td>s</td> <td>minimum time advantage</td> </tr> <tr> <td>16</td> <td>v_min(TAdv)_id1</td> <td>m/s</td> <td>Oncoming object’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>17</td> <td>a_min(TAdv)_id1</td> <td>m/s²</td> <td>Oncoming object’s acceleration in heading the moment of minimum time advantage</td> </tr> <tr> <td>18</td> <td>v_min(TAdv)_id2</td> <td>m/s</td> <td>vehicle’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>19</td> <td>a_min(TAdv)_id2</td> <td>m/s²</td> <td>vehicle’s acceleration in heading the minimum time advantage</td> </tr> <tr> <td>20</td> <td>THW_min(TAdv)_id1</td> <td>s</td> <td>Timeheadway oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>21</td> <td>THW_min(TAdv)_id2</td> <td>s</td> <td>Timeheadway vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>22</td> <td>d_min(TAdv)_id1</td> <td>m</td> <td>Distance oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>23</td> <td>d_min(TAdv)_id2</td> <td>m</td> <td>Distance vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> </tbody> </table> <p> </p>
The Intersectional Digital Inequality in Indonasia dataset
<p>The dataset presents the results of the research on intersectional digital inequality in Indonesia between the years 2018 and 2022.</p> <p>The raw data on internet access, signal availability and telecommunication expenditures were sourced from Badan Pusat Statistik - Statistic Office of Indonesia, published in the report Telecommunication Statistics in Indonesia (2022).</p> <p>Apart from one-dimensional or multi-dimensional groups averages, the dataset includes Equality Ratios and Intersectional Surplus computed according to the methodology proposed by Meili (2022).</p> <p>References:</p> <p><span>BPS-Statistics Indonesia, 2022. <em>Telecommunication Statistics in Indonesia. Technical Report.</em> Badan Pusat Statistik.</span></p> <p><span>Meili, D., Günther, I., Harttgen, K., 2022. Intersectional Inequality in Education, in: <em>37th IARIW General Conference.</em></span></p> <p> </p>
Neritina snails upstream migrations at the intersection of Rio Mameyes with road PR Route 3 (bridge 1771)
This data set includes N. virginea densities and sizes from two channels in lower Rio Mameyes under PR Route 3 bridge during the upstream migration season Aug-Dec 2000. Microhabitat use (near-bed water velocities and depth) within both channels is also included. Massive migrations in long trails occurring on the sloped concrete embankment of the main channel were also documented during 99 weeks. Individual size from migratory aggregations was measured during selected dates. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.
<p><strong>Background</strong></p> <p>Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes. </p> <p> </p> <p><strong>Methods and Results</strong></p> <p>To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (<em>SKI</em>, <em>KANK2</em>, <em>SORT1</em>), endothelial cells (<em>SLC44A1</em>, <em>ATP2B1</em>), and macrophages (<em>APOE</em>, <em>HNRNPUL1</em>). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes.</p> <p><br> <strong>Conclusion</strong></p> <p>We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.</p> <p> </p>
MaMo online Webinar Cycle "Materializing Modernity - Landscape, Architecture and Anthropology intersections in 20th-century rurality"
<p>A dataset (WP2-B_Materials_1) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_1 (PART 1) - Contents</p> <ul> <li>Programme of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>Banner of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>1st meeting - Introduction: MaMo - an introduction to Albanian Socialist and Post-Socialist rurality by Federica Pompejano, MSCA-IF Fellow, Department of Ethnology, Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, Albania<br> Oral presentation: "The Albanian Village as an anthropological encounter of modernity" by Nebi Bardhoshi, Associate Professor and Director of the Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, and Olsi Lelaj, Researcher, Department of Ethnology, IAKSA, Academy of Albanian Studies, Albania</li> <li>MaMo Webinar Cycle - 1st meeting banner</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 1st meeting Instagram post</li> <li>2nd meeting - Oral presentation: "Exploring Rurality in Southern Italy: the experience of 'Sonic Ethnography'" by Nicola Scaldaferri, Associate Professor, Department of Cultural and Environmental Heritage, Università Statale di Milano, Italy, and Lorenzo Ferrarini, Lecturer in Social and Visual Anthropology, Granada Centre for Visual Anthropology, University of Manchester, United Kingdom</li> <li>MaMo Webinar Cycle - 2nd meeting banner</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 2nd meeting Instagram post</li> </ul>
MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" - Part 2
<p>A dataset (WP2-B_Materials_2) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_2 (PART 2) - Contents</p> <ul> <li>3rd meeting - Oral presentation 1: "Embedding the Past into Modernist Rural Landscapes" by Cristina Pallini, Associate Professor, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy - Oral presentation 2: "Figures in a landscape: notes for a history of rural planning and village design in the Eastern bloc" by Axel Fischer, Associate Professor p.t., Université libre de Bruxelles, School of Architecture La Cambre Horta, hortence lab for architectural history, theory and criticism, Belgium (WP2-B_Webinar_03W.mpeg)</li> <li>MaMo Webinar Cycle - 3rd meeting banner (WP2-B_Webinar-03W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presentation abstract - 1 (WP2-B_Webinar_03W-Poster1.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presentation abstract - 2 (WP2-B_Webinar_03W-Poster2.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presenters short bio (WP2-B_Webinar_03W-Poster3.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting Instagram post - 1 (WP2-B_Webinar_03W_IG1.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting Instagram post - 2 (WP2-B_Webinar_03W_IG2.jpg)</li> </ul>
MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" - Part 3
<p>A dataset (WP2-B_Materials_3) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_3 (PART 3) - Contents</p> <ul> <li>4th meeting - Oral presentation: "Concepts integrated conservation as inroads to sustainable management of cultural landscapes" by Bosse Lagerqvist, Associate Professor and Senior Lecturer, Department of Conservation, Göteborgs Universitet, Sweden (WP2-B_Webinar_04W.mpeg)</li> <li>MaMo Webinar Cycle - 4th meeting banner (WP2-B_Webinar_04W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting poster with oral presentation abstract (WP2-B_Webinar_04W_Poster1.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting poster with oral presenter short bio (WP2-B_Webinar_04W_Poster2.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting Instagram post (WP2-B_Webinar_04W_IG.jpg)</li> <li>5th meeting - Oral presentation: "Socialist Modernism in the former Eastern Bloc (1955-1991), The Socialist Modernism map at socialistmodernism.com" by Dumitru Rusu, President of B.A.C.U. Association - Birou pentru Art si Cercetare Urbană (Bureau for Art and Urban Research), Romania (WP2-B_Webinar_05W.mpeg)</li> <li>MaMo Webinar Cycle - 5th meeting banner (WP2-B_Webinar_05W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting poster with oral presentation abstract (WP2-B_Webinar_05W_Poster1.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting poster with oral presenter short bio (WP2-B_Webinar_05W_Poster2.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting Instagram post (WP2-B_Webinar_05W_IG.jpg)</li> </ul>
VEDSIB: VEhicular trajectory Dataset at Signalised1 Intersection in Bremen Dataset
<p> VEDSIB dataset contains 26,007 trajectories recorded over 32 hours in a signalised intersection in Bremen, Germany.</p> <p>VEDSIB contains:</p> <ul> <li>Date: the date of the collected information</li> <li>Time: Time when the information was collected</li> <li>Direction: at the intersection, which direction the vehicle came from</li> <li>Number: the number of vehicles par minutes</li> <li>Type: The type of vehicles; either a car, a bus or a tram.</li> <li>Temperature in °F: the temperature observed at the time of collecting the information</li> <li>Humidity: humidity level</li> <li>Wind information (speed, Gust): the wind information</li> <li>Pressure, Precipitation, Condition: the general condition of the weather</li> <li>Rainy information: if there is rain or not at the time of collecting the data.</li> </ul>
Direct observation of geometric phase in dynamics around a conical intersection
<p>The CSV files contain experimental and theoretical data corresponding to figures 3 and A1 of the paper "Direct observation of geometric phase in dynamics around a conical intersection", available at <a href="http://arxiv.org/abs/2211.07320">arxiv:2211.07320</a>. </p> <p>The contents of the files are described in README.txt.</p>
Corresponding spreadsheet to the Paper 'An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council' submitted to the Scientometric journal by Roberta Ruggieri, Fabrizio Pecoraro and Daniela Luzi from National Research Council, Italy.
<p>Gender equality and Open Access (OA) are priorities within the European Research Area (ERA) and cross-cutting issues in European research program H2020. Gender and openness are also key elements of Responsible Research and Innovation (RRI). However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent, unrelated topics.<br> This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016-2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support.<br> Our results indicate that gender disparities in scientific production still persist in particularly in STEM disciplines (Science, Technology, Engineering and Mathematics), while in medical and agricultural sciences the gender gap is the closest to parity. A positive dynamic toward OA publishing and women scientific production is shown when open disciplines with well-established practices are related to articles supported by funds. A slightly higher women propensity toward OA is shown when considering Gold OA,OA or authorships with women in the first and last article by-line position. Moreover, the prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the week enforcement of the Italian OA policies.</p>
Data for "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge"
<p>Data sets underlying Figs. 1-4, and S1-S6, S8-S20 of the paper entitled "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge".</p>
Intersections between clinical variant databases
<p>The upset plots visualize the distribution of 990 unique validated variants from patients with MDS/AML detected in seven public datasets (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411) among clincal variant databases as well as the overlap between the different databases.</p> <p><strong>A</strong> Distribution of the variants with therapeutic implications (tier I)</p> <p><strong>B </strong>Distribution of the variants with diagnostic and/or prognostic implications or disease association (tier II)</p> <p><strong>C</strong> Distribution of the variants of unclear significance (tier III)<br> </p> <p><strong>List of abbreviations:</strong></p> <p>Cancer Genome Interpreter’s variants database (CGI)</p> <p>Clinical Interpretations of Variants in Cancer database (CIVIC)</p> <p>Clinical Variants database of the National Center for Biotechnology Information (ClinVar)</p> <p>Catalogue Of Somatic Mutations In Cancer (COSMIC)</p> <p>Database of Curated Mutation (DOCM)</p> <p>Human Gene Mutation Database (HGMD)</p> <p>Jackson Laboratory Clinical Knowledgebase (JAX)</p> <p>MolecularMatch database (MM)</p> <p>Oncology Knowledge Base (OncoKB)</p> <p>Pharmacogenomics Knowledgebase (PharmGKB)</p> <p>Phenotype for ENCODE (PhenCode)</p> <p> </p>
Intersectionality Spectrum - when DEI initiatives target gender equity
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. There is a dotted vertical line that separate visiable spectrum from invisible spectrum. There is an orange box that says “Most DEI initiatives target gender equity” and there are three orange arrows pointing to the white women symbol. This is because they benefit the most, and other non-white women do not, even though other non-white women have a higher degree of difficulty.</p>
Intersectionality Spectrum - triage like a hospital
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. It shows 3 arrows pointing down on the bars that have the highest degree of difficulty to signify that we need to prioritise support to those who need it most because they have been discriinated the most. It has one green arrow pointing down to those with smaller degrees of difficulty to signify we still need to help those people as well, but with less intensity or frequency. This is similar to how a hospital should triage patients, in that we need to look after the sickest people first.</p>
The hydro-mechanical properties of fracture intersections: pressure dependant permeability and effective stress law
<p>The present repository includes the raw permeability data presented in the manuscript submitted for publication in Journal of Geophysical Research: Solid Earth entitled <em>The hydro-mechanical properties of fracture intersections: pressure-dependant permeability and effective stress law.</em></p> <p>The repository is presented as compressed folders named after the Figures of the manuscript where the data is presented. </p>
Intersection Vehicular Traffic Flow Scheduling Datasets
<p><strong>Background</strong></p> <p>Vehicular traffic congestion remains a major problem in most modern cities of the world. Therefore, efforts to minimize these congestions and their accompanying effects continuously receive much attention from researchers, traffic engineers, policymakers, etc.</p> <p>One important element required in the efforts of traffic engineering for the minimization of traffic congestion is data. Data influence the modeling and deployment of traffic scheduling systems. Most importantly, the objectives of modeling and deploying the traffic scheduling system influence the nature of the data to be used.</p> <p><strong>Aim</strong></p> <p>The aim of this study was to generate and provide a dataset that may be used in the training of computationally intelligent systems having basic objects of minimizing waiting time, travel time, etc. at various intersections (isolated intersections or roundabouts).</p> <p><strong>Methodology</strong></p> <p>The dataset (<strong><em>.csv</em></strong>) consisted of waiting time (W), queue length (Q), and phase duration (P). The waiting time is the time duration vehicles have waited at an intersection/roundabout before being scheduled to utilize the intersection/roundabout. The queue length refers to the number of vehicles (vehicular count) waiting at an intersection/roundabout. Phase duration is the time period a given vehicular flow (lane) is assigned the green wave to utilize the intersection/roundabout. The dataset was obtained through repeated training, testing, and modification of phase duration and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model.</p> <p>The dataset considered bounded conditions on the three parameters (W, Q, P). The W and Q were bounded between zero and ninety – [0, 90] and the P is [13, 50]. That is, when W and Q are greater than or equal to the upper bound, the upper bound is used. Every flow may be assigned a minimum P of 13s and a maximum of 50s. The P-bounds assumed that the lower bound is large enough for vehicles on the assigned traffic flow to move to the safe region of the intersection before the scheduling system switches assignment to another traffic flow. </p> <p><strong>Conclusion</strong></p> <p>The dataset may be used as a benchmark dataset for the improvement of traffic flow controllers as well as other datasets.</p>
Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization
<p>Single-molecule localization microscopy (SMLM) often suffers from suboptimal resolution due to imperfect drift correction. Existing marker-free drift-correction algorithms often struggle to reliably track high-frequency drift and lack the computational efficiency to manage large, high-throughput localization datasets. We present an adaptive intersection maximization-based method (AIM) that leverages the entire dataset's information content to minimize drift correction errors, particularly addressing high-frequency drift, thereby enhancing the resolution of existing SMLM systems. We demonstrate that AIM can robustly and efficiently achieve an angstrom-level tracking precision for high-throughput SMLM datasets under various imaging conditions, resulting in an optimal resolution in simulated and biological experimental datasets. We offer AIM as simple and model-free software for instant resolution enhancement with standard CPU devices.</p>
SUMO intersection model and vehicle trip data
<p>There are two parts of the data: 1) a SUMO model of a typical intersection that consists of 4 approaches, each of which consists of 3 movements (left turn, right turn, and straight); 2) the vehicle trip information data generated by SUMO under different volume files, which is used to train the intersection signal control algorithm.</p> <p>The SUMO model contains five ".xml" files (node, edge, connection, net, and additional files) which are used to construct and configure the model. One can refer to the official SUMO tutorial for the format and functions of these files: (<a href="https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo">https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo</a>) </p> <p>The vehicle trip data is generated by SUMO as an output (which is specified in ".sumocfg" file). One can refer to the official tutorial (<a href="https://sumo.dlr.de/wiki/Simulation/Output/TripInfo">https://sumo.dlr.de/wiki/Simulation/Output/TripInfo</a>) to understand the data format.</p> <p>Note that readers capable to read ".xml" files like Notepad++ are required to read the SUMO model and vehicle trip data.</p>
Text-fig. 3. Scatter diagram of mean L/W values of selected Spermophilinus upper first molars (M1 and M2) from Switzerland and south Germany. The crosses correspond to the minimum and maximum values of the length and width. The intersection gives the value of the mean. Comparative data are after Ziegler and Fahlbusch (1986), Bolliger (1992), Kälin (1993), Ziegler (1995, 2005), Kälin and Engesser (2001), Prieto (2007), Prieto et al. (2009, 2017), Seehuber (2009). Blue: localities younger than ~14 Ma (S. bredai); Green: localities ranging from ~14 Ma to ~15Ma (S. bredai); Red: localities ranging from ~15 Ma to ~16 Ma (S. besana); Yellow: older localities (most S. besana). in Dental Anomaly In A Middle Miocene Fossil Of The Genus Spermophilinus (Rodentia, Sciuridae) From Southern Germany
Text-fig. 3. Scatter diagram of mean L/W values of selected Spermophilinus upper first molars (M1 and M2) from Switzerland and south Germany. The crosses correspond to the minimum and maximum values of the length and width. The intersection gives the value of the mean. Comparative data are after Ziegler and Fahlbusch (1986), Bolliger (1992), Kälin (1993), Ziegler (1995, 2005), Kälin and Engesser (2001), Prieto (2007), Prieto et al. (2009, 2017), Seehuber (2009). Blue: localities younger than ~14 Ma (S. bredai); Green: localities ranging from ~14 Ma to ~15Ma (S. bredai); Red: localities ranging from ~15 Ma to ~16 Ma (S. besana); Yellow: older localities (most S. besana).
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
<p>A pre-trained model of the paper "Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections," CoRL 2020.</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.