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4,182 results for “universities”
University of Houston Coastal Center soil carbon stocks in 2022
This study sampled plant community composition and soil carbon stocks at two native prairie sites in the Houston metropolitan area. Some of the samples were taken in areas that had been invaded by forest, and in some cases treated to remove invasive species.
Context dependence of grassland plant mycorrhizal response, University of Kansas, 2021
Many of the disturbance-sensitive, late successional plant species in grasslands respond to arbuscular mycorrhizal (AM) fungi more positively via growth and establishment than plants that readily establish in disturbed areas (i.e. early successional species). Inoculation with AM fungi can therefore aid the establishment of late successional species in disturbed areas. If the differential benefit of AM fungi to late vs. early successional plants is context-dependent, however, this advantage could be diminished in high phosphorus (P) post-agricultural soils or in future climates with altered precipitation. In this greenhouse experiment, we tested if late successional plant species are less plastic in their reliance on AM fungi than early successional plants by growing 17 tallgrass prairie plant species of different successional status (9 early and 8 late successional) in full factorial combinations of inoculated or uninoculated with AM fungi, with ambient or high P levels, and with low or high levels of water.
Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Michigan Rodents Distribution at the University of Michigan Biological Station, Pellston, MI, from 1883 to 2007
Distribution of rodents in northern Michigan documented by Dr. Phil Myers, et al. We use museum and other collection records to document large and extraordinarily rapid changes in the ranges and relative abundance of 9 species of mammals in the northern Great Lakes region (white-footed mice, woodland deer mice, southern red-backed voles, woodland jumping mice, eastern chipmunks, least chipmunks, southern flying squirrels, northern flying squirrels, common opossums). These species reach either the southern or the northern limit of their distributions in this region. Changes consistently reflect increases in species of primarily southern distribution (white-footed mice, eastern chipmunks, southern flying squirrels, common opossums) and declines by northern species (woodland deer mice, southern red-backed voles, woodland jumping mice, least chipmunks, northern flying squirrels). White-footed mice and southern flying squirrels have extended their ranges over 225 km since 1980, and at particularly well-studied sites in Michigan's Upper Peninsula, small mammal assemblages have shifted from numerical domination by northern species to domination by southern species. Repeated re-sampling at some sites suggests that southern species are replacing northern ones rather than simply being added to the fauna. Observed changes are consistent with predictions from climatic warming but not with predictions based on recovery from logging or changes in human populations. Because of the abundance of these focal species (the 8 rodent species make up 96.5% of capture records of all forest-dwelling rodents in the region and 70% of capture records of all forest-dwelling small mammals) and the dominating ecological roles they play, these changes substantially affect the composition and structure of forest communities. They also provide an unusually clear example of change that is likely to be the result of climatic warming in communities that are experienced by large numbers of people.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
Flowering phenology along the University of Wisconsin Milwaukee Field Station boardwalk in the Cedarburg Bog Saukville WI 1984 to 1993
Flowering phenology of 189 plant species was studied over eight growing seasons between 1984 and 1993. Most of the phenological observations were made along the University of Wisconsin-Milwaukee Field Station’s boardwalk in the Cedarburg Bog. The Cedarburg Bog is the one of the largest intact wetlands in southeastern WI (c. 1000 ha). It remains relatively undisturbed because of a long history of protection and contains a diversity of plant species. The boardwalk extends through several distinct vegetation types that are found within the bog and over upland forested habitat on islands the occur within the bog. This study therefore represents a broad sample of the flora found within the wetland, although the species observed concentrate primarily on showy species and exclude grasses and sedges. Observations of flowering phenology were made generally every 7-10 days during the growing season.
Ecohydrological monitoring of urban ecological infrastructure at the Arizona State University Tempe Campus (Tempe, Arizona)
Urban Ecological Infrastructure (UEI) are urban ecological structures whose ecological functions are increasingly being used to deliver key urban services and address urban sustainability challenges. The growing use of UEI to address urban sustainability challenges can bring together teams of urban researchers and practitioners to co-produce UEI design, monitoring and maintenance. However, this co-production process received little attention in the literature, and has not been studied in the Phoenix Metro Area. This study examined several components of a co-produced design process and related project outcomes associated with a small-scale UEI project – bioretention basins installed at the Arizona State University (ASU) Orange Mall and Student Pavilion in Tempe, AZ. We explored the design process associated with the collaborative development of an ecohydrological monitoring protocol for assessment of post-construction performance of this UEI system. This co-production process involved both CAP LTER researchers and practitioners, designers, and managers at ASU and associated third-party consultants involved in site design and management. Together, these researchers and practitioners co-produced a suite of ecohydrological metrics to monitor the performance of the bioretention basins at Orange Mall. Specifically, this protocol evaluated the performance of the UEI system with regards to two of it’s key design goals: storm water capture and storm water quality improvement. The ecohydrological data produced by the implementation of this monitoring protocol are presented here. Monitoring equipment were installed throughout the site in June and July 2018. Site monitoring and data collection began in August 2018, and continued through Feb 2019.
Additional CPT's Raw Data performed by Istanbul University-Cerrahpasa in scope of LIQUEFACT project (H2020, 700748)
<p>Additional CPT's Raw Data performed by Istanbul University-Cerrahpasa in scope of LIQUEFACT project (H2020, 700748)</p>
Datasets for ``Numerical Simulations of Gravitational Waves from Early-Universe Turbulence''
<pre>The tar archive GW.tar contains an index.html file with links to the run directories for each run in Table 1 of the paper "Numerical Simulations of Gravitational Waves from Early-Universe Turbulence" by A. Roper Pol, S. Mandal, A. Brandenburg, T. Kahniashvili, &amp; A. Kosowsky with the temporary URL http://norlx55.nordita.org/~brandenb/tmp/GW. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/GW/</pre>
University of Oklahoma CopterSonde Files from LAPSE-RATE
<p>This dataset contains quality-controlled thermodynamic (temperature, pressure, humidity) and kinematic (wind speed and direction) measurements from the University of Oklahoma Center for Autonomous Sensing and Sampling team during the LAPSE-RATE campaign from 14--19 July 2018. These files are from the fleet of 3 individual CopterSonde rotary-wing UAS used during the campaign. Differences between them have been accounted for through calibration. See README_v1.txt for more information.</p>
Sparkman: A Smart Parking Management Tool For University Campuses
<p>PI Kelvin Cheu and his students have developed a total parking demand model for students and a “base price” model for student parking permits based on data gathered from an internet survey on parking at 208 university campuses across the United States. This project aims to develop a student parking lot zoning and zone permit pricing model, and then integrate the total demand model, “base price” model and the zoning model into a software tool named Sparkman, which can be used by all university parking offices.</p>
S72 | NTUPHTW | Pharmaceutically Active Substances Suspect List from National Taiwan University
<p>This is the collection associated with list S72 NTUPHTW Pharmaceutically Active Substances Suspect List from National Taiwan University on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A suspect list based on Agilent PCDLs containing pharmaceutically active substances from the College of Public Health, National Taiwan University, kindly provided by Wen-Ling Chen. Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3955664">10.5281/zenodo.3955664</a>.</p>
Free Universal Sound Separation Dataset
<p>The Free Universal Sound Separation (FUSS) Dataset is a database of arbitrary sound mixtures and source-level references, for use in experiments on arbitrary sound separation. </p> <p>This is the official sound separation data for the DCASE2020 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments.</p> <p><strong>Citation: </strong>If you use the FUSS dataset or part of it, please cite our paper describing the dataset and baseline [1]. FUSS is based on <a href="https://annotator.freesound.org/fsd/">FSD data</a> so please also cite [2]:</p> <p><strong>Overview: </strong>FUSS audio data is sourced from a pre-release of <a href="https://annotator.freesound.org/fsd/">Freesound dataset</a> known as (FSD50k), a sound event dataset composed of Freesound content annotated with labels from the AudioSet Ontology. Using the FSD50K labels, these source files have been screened such that they likely only contain a single type of sound. Labels are not provided for these source files, and are not considered part of the challenge. For the purpose of the DCASE Task4 Sound Separation and Event Detection challenge, systems should not use FSD50K labels, even though they may become available upon FSD50K release.</p> <p>To create mixtures, 10 second clips of sources are convolved with simulated room impulse responses and added together. Each 10 second mixture contains between 1 and 4 sources. Source files longer than 10 seconds are considered "background" sources. Every mixture contains one background source, which is active for the entire duration. We provide: a software recipe to create the dataset, the room impulse responses, and the original source audio.</p> <p><strong>Motivation for use in DCASE2020 Challenge Task 4: </strong> This dataset provides a platform to investigate how source separation may help with event detection and vice versa. Previous work has shown that universal sound separation (separation of arbitrary sounds) is possible [3], and that event detection can help with universal sound separation [4]. It remains to be seen whether sound separation can help with event detection. Event detection is more difficult in noisy environments, and so separation could be a useful pre-processing step. Data with strong labels for event detection are relatively scarce, especially when restricted to specific classes within a domain. In contrast, source separation data needs no event labels for training, and may be more plentiful. In this setting, the idea is to utilize larger unlabeled separation data to train separation systems, which can serve as a front-end to event-detection systems trained on more limited data.</p> <p><strong>Room simulation: </strong>Room impulse responses are simulated using the image method with frequency-dependent walls. Each impulse corresponds to a rectangular room of random size with random wall materials, where a single microphone and up to 4 sources are placed at random spatial locations.</p> <p><strong>Recipe for data creation: </strong>The data creation recipe starts with scripts, based on<a href="https://github.com/justinsalamon/scaper"> scaper</a> [5], to generate mixtures of events with random timing of source events, along with a background source that spans the duration of the mixture clip. The scipts for this are at<a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss"> this GitHub repo</a>.</p> <p>The data are reverberated using a different room simulation for each mixture. In this simulation each source has its own reverberation corresponding to a different spatial location. The reverberated mixtures are created by summing over the reverberated sources. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.</p> <p>The constituent source files for each mixture are also generated for use as references for training and evaluation. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.</p> <p>Note: no attempt was made to remove digital silence from the freesound source data, so some reference sources may include digital silence, and there are a few mixtures where the background reference is all digital silence. Digital silence can also be observed in the event recognition public evaluation data, so it is important to be able to handle this in practice. Our evaluation scripts handle it by ignoring any reference sources that are silent. </p> <p><strong>Format: </strong>All audio clips are provided as uncompressed PCM 16 bit, 16 kHz, mono audio files.</p> <p><strong>Data split: </strong> The FUSS dataset is partitioned into "train", "validation", and "eval" sets, following the same splits used in FSD data. Specifically, the train and validation sets are sourced from the FSD50K dev set, and we have ensured that clips in train come from different uploaders than the clips in validation. The eval set is sourced from the FSD50K eval split.</p> <p><strong>Baseline System: </strong>A baseline system for the FUSS dataset is available at <a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss">dcase2020_fuss_baseline</a>.</p> <p><strong>License: </strong>All audio clips (i.e., in FUSS_fsd_data.tar.gz) used in the preparation of Free Universal Source Separation (FUSS) dataset are designated Creative Commons (CC0) and were obtained from<a href="http://freesound.org"> freesound.org</a>. The source data in FUSS_fsd_data.tar.gz were selected using labels from the<a href="https://annotator.freesound.org/fsd/"> FSD50K corpus</a>, which is licensed as Creative Commons Attribution 4.0 International (CC BY 4.0) License.</p> <p>The FUSS dataset as a whole, is a curated, reverberated, mixed, and partitioned preparation, and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This license is specified in the `LICENSE-DATASET` file downloaded with the `FUSS_license_doc.tar.gz` file.</p> <p><strong>Notes:</strong></p> <p>Added in v1.2: </p> <ul> <li>FUSS_baseline_dry_model.tar.gz: baseline separation model trained on non-reverberated (dry) data. </li> <li>FUSS_DESED_baseline_dry_2_model.tar.gz:: baseline separation model for the DESED task, trained on a mixture of DESED in-domain data and FUSS data</li> </ul> <p>Added in v1.3:</p> <ul> <li>FUSS_DESED_baseline_dry_1_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED mixtures from dry FUSS mixtures (DmFm)</li> <li>FUSS_DESED_baseline_dry_4_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, dry FUSS mixture, and 5 DESED foreground sources with PIT (PIT)</li> <li>FUSS_DESED_baseline_dry_4np_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 10 DESED classes, and dry FUSS mixture without PIT (Classwise)</li> <li>FUSS_DESED_baseline_dry_6_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 5 DESED foreground sources, 4 dry FUSS sources, with groupwise PIT (GroupPIT)</li> </ul> <p>The names in parentheses are the task names from Table 3 of the following paper: <a href="https://arxiv.org/pdf/2007.03932.pdf">Nicolas Turpault, Scott Wisdom, Hakan Erdogan, John R. Hershey, Romain Serizel, Eduardo Fonseca, Prem Seetharaman, and Justin Salamon, "Improving Sound Event Detection in Domestic Environments using Sound Separation", DCASE 2020.</a></p>
Tabular summary of University dataset
<p>This is Public versus Private Universities acceptance data set. It can be used for cluster analysis, data visualization, management, analysis, and predictions.</p>
Performance Indicator Data for Eight Research Universities in Africa 2001 to 2015
<p>Student, staff and research performance indicator data for eight research universities in Africa from 2001 to 2015, collected by the Higher Education Research and Advocacy Network for Africa (HERANA), a project of the Centre for Higher Education Trust (CHET).</p>
South African University Responses to Transformation
<p>Dataset describing how universities in South Africa have responded to demands for transformation. Data are for 2018 or most recent year. Fields include: University; Organisational structure for transformation; URL; Institutional transformation plan [ITP]; Most recent year; Key transformation areas or goals in ITP; Other documents with transformation plans, reports, targets; Measurable targets [Y, N]; Targets for no. or % of black students [Year]; Targets for no. or % of black academic staff [Year]</p>
Closure of the 8th European Summer University
<p>Closure of the 8th European Summer University</p>
Universal metabolic constraints shape the evolutionary ecology of diving in animals
<p>Diving as a lifestyle has evolved on multiple occasions when air-breathing terrestrial animals invaded the aquatic realm, and diving performance shapes the ecology and behaviour of all air-breathing aquatic taxa, from small insects to great whales. Using the largest dataset yet assembled, we show that maximum dive duration increases predictably with body mass in both ectotherms and endotherms. Compared to endotherms, ectotherms can remain submerged for longer, but the mass scaling relationship for dive duration is much steeper in endotherms than in ectotherms. These differences in diving allometry can be fully explained by inherent differences between the two groups in their metabolic rate and how metabolism scales with body mass and temperature. Therefore, we suggest that similar constraints on oxygen storage and usage have shaped the evolutionary ecology of diving in all air-breathing animals, irrespective of their evolutionary history and metabolic mode. The steeper scaling relationship between body mass and dive duration in endotherms not only helps explain why the largest extant vertebrate divers are endothermic rather than ectothermic, but also fits well with the emerging consensus that large extinct tetrapod divers (e.g. plesiosaurs, ichthyosaurs and mosasaurs) were endothermic.</p>
4 Mars 1848 "Universal male suffrage" 10/12
Adoption of universal male suffrage. The process is in effect the sessions of March 2 and March 4, where the principle of universal suffrage is accepted, then the March 5 when the decree is adopted. The work is sometimes supposed to represent the abolition of slavery15, by confusion with the decree of the Provisional Government dated March 4, 1848 which instituted a "commission to prepare, as soon as possible, the act of immediate emancipation in all the colonies of the Republic, and who appointed Victor Schoelcher its president; but the decree which actually instituted the abolition of slavery dates from April 27, 1848. Source: Objaverse 1.0 / Sketchfab
Generative AI in University Communication - Survey Data (June 2023)
<p>Der Datensatz mit dem Titel "Generative KI in der Hochschulkommunikation - Umfragedaten (Juni 2023)" erfasst Informationen zur Einführung und Nutzung von generativer künstlicher Intelligenz (KI) im Kontext der Hochschulkommunikation. Die Umfrage, die im Juni 2023 unter 318 deutschen Hochschulen durchgeführt wurde, von denen 101 geantwortet haben, untersucht verschiedene Aspekte, darunter Bekanntheit und Wissen über verschiedene KI-Tools (z.B. ChatGPT), Diskussionen in Gremien, das Vorhandensein von Richtlinien für die Nutzung, das Vorhandensein von Arbeitsgruppen für generative KI, strategische Ziele und Initiativen, Schulungsangebote für generative KI-Tools und die wahrgenommene Bedeutung von generativen KI-Tools in der Hochschulkommunikation. Ziel des Datensatzes ist es, Einblicke in die aktuelle Landschaft und Praxis der Integration generativer KI im universitären Umfeld zu geben.</p><p>The dataset, titled "Generative AI in University Communication - Survey Data (June 2023)," captures information related to the adoption and utilization of generative artificial intelligence (AI) in the context of university communication. This survey, conducted in June 2023 among 318 German universities of which 101 responded, explores various aspects, including awarenes and knowledge of various AI tools (e.g. ChatGPT), discussions in committees, the existence of guidelines for usage, the presence of working groups for generative AI, strategic goals and initiatives, training offerings for generative AI tools, and the perceived importance of generative AI tools in university communication. The dataset aims to provide insights into the current landscape and practices regarding the integration of generative AI within university settings.</p><p> </p><p>More information here: <a href="https://www.hof.uni-halle.de/projekte/hochki/">https://www.hof.uni-halle.de/projekte/hochki/</a></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.