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3,538 results for “Common”
Zooplankton and macroinvertebrate size spectra, biomass, and community composition; and harvest of bigmouth buffalo and common carp in six shallow lakes in Iowa, USA (2018-2020)
This data product contains biological data collected within six shallow lakes in Iowa, USA between 2018 - 2020, where four lakes were undergoing targeted removals of common carp (Cyprinus carpio) and bigmouth buffalo (Ictiobus cyprinellus). Parts of these data were a portion of Albright et al. 2022 (https://doi.org/10.6073/pasta/1d3797fd573208bae6f78963479445a0), however the data herein include additional survey data from the Ambient Lake Monitoring network instituted through Iowa State University and the Iowa Department of Natural Resources (https://www.iowadnr.gov/environmental-protection/water-quality/water-monitoring/ambient-lake-monitoring#ambient-lake-monitoring-sampling-plan). Data are packaged and formatted specifically for size spectra analysis and compositional analysis.
Shared neural codes for visual and semantic information about familiar faces in a common representational space
Open the record for dataset details and reuse information.
EISCAT Svalbard radar Common Program data from February 26 to February 28 2023, which is processed by GUISDAP
<p>This is two-dimensional (time and altitude) ionospheric parameter data that contains electron density, electron temperature and ion temperature. It is estimated based on EISCAT Svalbard radar measurement implemeted as common program from February 26 to Feburuary 28, 2023 (https://portal.eiscat.se/) and processed by a software for incoherent scatter radar analysis, GUISDAP (https://gitlab.com/eiscat/guisdap9). The more detailed descriptions can be found as metadata in the uploaded netCDF file.</p>
Journey North - Common Loon and Ice-Out observations by volunteer community scientists across North America (1997-2020)
This data package contains Common Loon migration and ice melt data consisting of 9,800 total observational reports from 1997 - 2020 across North America. These data were collected by 1,437 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Loon and Ice-Out Project is an ongoing study of loon and ice melt phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates of Common Loons (Gavia immer) and ice that has melted from bodies of water in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Common Loon and Ice-Out Project Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Root fungi isolated from common Louisiana marsh plants 2017-18.
Nearly all plants are colonized by fungal endophytes, and a growing body of work shows that both environment and host species shape plant-associated fungal communities. However, few studies place their work in a phylogenetic context to understand endophyte community assembly through an evolutionary lens. Here we collected data to investigate environmental and host effects on root endophyte assemblages in coastal Louisiana marshes. We isolated and sequenced culturable fungal endophytes from roots of three-four dominant plant species from each of three sites of varying salinity. We provide data on abundance and taxonomy of the isolated fungal taxa as well as phylogenetic diversity (mean phylogenetic distance, MPD) and phylogenetic composition (based on MPD).
Growth data for young of the year arctic grayling raised in a aquatic common garden at Toolik Field Station, summer 2017
Since 2009, the FISHSCAPE Project (Grant #1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream); The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Ailish and Atigun Rivers and the Galbraith Lakes); and The Itkillik (primarily the I-Minus outlet stream, a tributary that that feeds into the Itkilik River). The goals are to understand and predict the adaptability and persistence of a key Arctic species, the Arctic grayling (Thymallus arcticus), to changing climate and hydrology. Research questions include: (1) Does landscape structure determine movement within and among watersheds; (2) do populations adapt to stream characteristics at local and regional scales; and (3) will the relative adaptability of populations determine their persistence under future climate change. To test ideas about local adaptation, we conducted an aquatic common garden experiment at Toolik Field Station, to investigate the genetic component of phenotypic variation among Arctic grayling populations on Alaska's North Slope. This file contains the growth data.
Survivorship data for young of the year Arctic grayling raised in an aquatic common garden at Toolik Field Station, summer 2017
Since 2009, the FISHSCAPE Project (grant # 1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream); The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Ailish and Atigun Rivers and the Galbraith Lakes); and The Itkillik (primarily the I-Minus outlet stream, a tributary that that feeds into the Itkilik River). The goals are to understand and predict the adaptability and persistence of a key Arctic species, the Arctic grayling (Thymallus arcticus), to changing climate and hydrology. Research questions include: (1) Does landscape structure determine movement within and among watersheds; (2) do populations adapt to stream characteristics at local and regional scales; and (3) will the relative adaptability of populations determine their persistence under future climate change. To test ideas about local adaptation, we conducted an aquatic common garden experiment at Toolik Field Station, to investigate the genetic component of phenotypic variation among Arctic grayling populations on Alaska's North Slope. This file contains the survivorship data.
Mean radiant temperature along a common route for people experiencing homelessness in downtown Phoenix, Arizona (USA) on August 20, 2024
This tabular dataset contains mean radiant temperature (Tmrt) measurements collected using MaRTy, a mobile biometeorological, along a route frequently traveled by people experiencing homelessness in downtown Phoenix, Arizona (USA). It includes Tmrt, air temperature (Tair), relative humidity (RH), wind speed, and wind direction at pedestrian height at 2-second intervals for a typical summer day (August 20, 2024; peak air temperature of 43.3 degrees Celsius) at 0700, 1300, and 1700 (local times). This dataset can inform heat mitigation strategies for vulnerable populations in Phoenix.
Long-term Memory (LTM) for famous Faces, Places, and common Objects
Open the record for dataset details and reuse information.
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
Dataset for "Common Propaganda Devices in Late Republican Coinage, 79-31 BCE"
<p>Dataset for "Common Propaganda Devices in Late Republican Coinage, 79-31 BCE" published in "Numismatica e Antichità Classiche" 50, 2021</p>
DATASET: characterization of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel
<p>Characterizarion of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel</p>
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions
<p>This data set corresponds to the paper: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions [1] (Experiment: Comprehensive reporting).</p> <p>The key research questions corresponding to this data set were:</p> <p>RQ1: What is the role of challenges for the field of biomedical image analysis (e.g. How many challenges conducted to date? In which fields? For which algorithm categories? Based on which modalities?)</p> <p>RQ2: What is common practice related to challenge design (e.g. choice of metric(s) and ranking methods, number of training/test images, annotation practice etc.)? Are there common standards?</p> <p>RQ3: Does common practice related to challenge reporting allow for reproducibility and adequate interpretation of results?</p> <p>To address these research questions, we aimed to capture all biomedical image analysis challenges that have been conducted up to 2016. To acquire the data, we analyzed the websites hosting/representing biomedical image analysis challenges, namely grand-challenge.org, dreamchallenges.org and kaggle.com as well as websites of main conferences in the field of biomedical image analysis, namely Medical Image Computing and Computer Assisted Intervention (MICCAI), International Symposium on Biomedical Imaging (ISBI), International Society for Optics and Photonics (SPIE) Medical Imaging, Cross Language Evaluation Forum (CLEF), International Conference on Pattern Recognition (ICPR), The American Association of Physicists in Medicine (AAPM), the Single Molecule Localization Microscopy Symposium (SMLMS) and the BioImage Informatics Conference (BII). This yielded a list of 150 challenges with 549 tasks.</p> <p>Next, a tool for instantiating the challenge parameter list introduced in [1] was used by some of the authors (engineers and medical student) to formalize all challenges that met our inclusion criteria as follows: (1) Initially, each challenge was independently formalized by two different observers. (2) The formalization results were automatically compared. In ambiguous cases, when the observers could not agree on the instantiation of a parameter - a third observer was consulted, and a decision was made. When refinements to the parameter list were made, the process was repeated for missing values. Based on the formalized challenge data set, a descriptive statistical analysis was performed to characterize common practice related to challenge design and reporting.</p> <p>[1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., Maier, O., Maier-Hein, K., Menze, B. H., Müller, H., Neher, P. F., Niessen, W., Rajpoot, N., Sharp, G. C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Aziz Taha, A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A.: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. arXiv preprint arXiv:1806.02051 (2018).</p>
Audio Commons Ground Truth Data for deliverables D4.4, D4.10 and D4.12
<p>This dataset contains the ground truth data used to evaluate the musical <strong>pitch</strong>, <strong>tempo</strong> and <strong>key </strong>estimation algorithms developed during the AudioCommons H2020 EU project and which are part of the <a href="https://www.audiocommons.org/2018/07/15/audio-commons-audio-extractor.html">Audio Commons Audio Extractor tool</a>. It also includes ground truth information for the <strong>single-event<em>ness</em> </strong>audio descriptor also developed for the same tool.</p> <p>This ground truth data has been used to generate the following documents:</p> <ul> <li><strong>Deliverable D4.4</strong>: Evaluation report on the first prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.10</strong>: Evaluation report on the second prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.12</strong>: Release of tool for the automatic semantic description of music samples</li> </ul> <p>All these documents are available in the <a href="https://www.audiocommons.org/materials/">materials section </a>of the AudioCommons website.</p> <p>All ground truth data in this repository is provided in the form of CSV files. Each CSV file corresponds to one of the individual datasets used in one or more evaluation tasks of the aforementioned deliverables. This repository <strong>does not include the audio files</strong> of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.</p> <p><br> <strong>Structure of the CSV files</strong></p> <p>All CSV files in this repository (with the sole exception of <em>SINGLE EVENT - Ground Truth.csv</em>) feature the following 5 columns:</p> <ol> <li><strong>Audio reference</strong>: reference to the corresponding audio file. This will either be a string withe the <strong>filename</strong>, or the <strong>Freesound ID </strong>(for one dataset based on Freesound content). See below for details about how to obtain those files. </li> <li><strong>Audio reference type</strong>: will be one of <em>Filename</em> or <em>Freesound ID</em>, and specifies how the previous column should be interpreted. </li> <li><strong>Key annotation</strong>: tonality information as a string with the form "RootNote minor/major". Audio files with no ground truth annotation for tonality are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10.</li> <li><strong>Tempo annotation</strong>: tempo information as an integer representing beats per minute. Audio files with no ground truth annotation for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10. Note that integer values are used here because we only have tempo annotations for <em>music loops</em> which typically only feature integer tempo values.</li> <li><strong>Pitch annotation</strong>: pitch information as an integer representing the MIDI note number corresponding to annotated pitch's frequency. Audio files with no ground truth pitch for tempo are left blank. Ground truth annotations are parsed from the original data source as described in the text of deliverables D4.4 and D4.10.</li> </ol> <p>The remaining CSV file, <em>SINGLE EVENT - Ground Truth.csv</em>, has only the following 2 columns:</p> <ul> <li><strong>Freesound ID</strong>: sound ID used in Freesound to identify the audio clip.</li> <li><strong>Single Event: </strong>boolean indicating whether the corresponding sound is considered to be a single event or not. Single event annotations were collected by the authors of the deliverables as described in deliverable D4.10.</li> </ul> <p> </p> <p><strong>How to get the audio data</strong></p> <p>In this section we provide some notes about how to obtain the audio files corresponding to the ground truth annotations provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these ground truth annotations.</p> <ul> <li><strong>Apple Loops (APPL)</strong>: This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#appl">provided here</a>. </li> <li><strong>Carlos Vaquero Instruments Dataset (CVAQ)</strong>: This dataset includes single instrument recordings carried out by <a href="https://www.linkedin.com/in/carlosvaquero/">Carlos Vaquero</a> as part of this <a href="http://mtg.upf.edu/node/2609">master thesis</a>. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs</li> <li><strong>Freesound Loops 4k (FSL4)</strong>: This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#instructions-for-setting-up-datasets">provided here</a>.</li> <li><strong>Giant Steps Key Dataset (GSKY)</strong>: This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations <a href="https://github.com/GiantSteps/giantsteps-key-dataset">available here</a>.</li> <li><strong>Good-sounds Dataset (GSND)</strong>: This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are <a href="https://zenodo.org/record/820937#.XEYMiy2ZN25">available here</a>.</li> <li><strong>University of IOWA Musical Instrument Samples (IOWA)</strong>: This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by <a href="http://theremin.music.uiowa.edu/MIS.html">visiting this website</a>.</li> <li><strong>Mixcraft Loops (MIXL)</strong>: This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#mixl">provided here</a>.</li> <li><strong>NSynth Dataset Test and Validation sets (NSYT and NSYV)</strong>: NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is <a href="https://magenta.tensorflow.org/datasets/nsynth">available here</a>.</li> <li><strong>Philarmonia Orchestra Sound Samples Dataset (PHIL)</strong>: This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the <a href="http://www.philharmonia.co.uk/explore/sound_samples">philarmonia orchestra website</a>.</li> <li><strong>Freesound Single Events Dataset (SINGLE EVENT)</strong>: This includes a selection of Freesound audio clips representing audio signals containing either a single audio <em>event</em> or multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#getting-fsl4-by-downloading-content-from-freesound">here</a> could be followed.</li> </ul>
Audio Commons Estimation Results Data for deliverables D4.4, D4.10 and D4.12
<p>This dataset contains the results of running the automatic audio annotation algorithms for <strong>pitch</strong>, <strong>tempo</strong> and <strong>key </strong>used for the evaluation of algorithms developed during the AudioCommons H2020 EU project and which are part of the <a href="https://www.audiocommons.org/2018/07/15/audio-commons-audio-extractor.html">Audio Commons Audio Extractor tool</a>. It also includes estimation results information for the <strong>single-event<em>ness</em> </strong>audio descriptor also developed for the same tool.</p> <p>These estimation results data has been used to generate the following documents:</p> <ul> <li><strong>Deliverable D4.4</strong>: Evaluation report on the first prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.10</strong>: Evaluation report on the second prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.12</strong>: Release of tool for the automatic semantic description of music samples</li> </ul> <p>All these documents are available in the <a href="https://www.audiocommons.org/materials/">materials section </a>of the AudioCommons website.</p> <p>All data in this repository is provided in the form of CSV files. Each CSV file corresponds to the analysis results of one musical task and one of the individual datasets used in the aforementioned deliverables. This repository <strong>does not include the audio files </strong>of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.</p> <p><br> <strong>Structure of the CSV files</strong></p> <p>All the CSV files in this repository (with the sole exception of <em>SINGLE EVENT - Estimation Results Truth.csv</em>) are named according to the following convention: "<em>DATASET_NAME</em> - <em>ESTIMATION_TASK</em> Estimation Results.csv". Therefore, estimation results for pitch, tempo and tonality music tasks are separated in different files. All these files share the same structure for the first 2 CSV columns:</p> <ol> <li><strong>Audio reference</strong>: reference to the corresponding audio file. This will either be a string withe the <strong>filename</strong>, or the <strong>Freesound ID </strong>(for one dataset based on Freesound content). See below for details about how to obtain those files. </li> <li><strong>Audio reference type</strong>: will be one of <em>Filename</em> or <em>Freesound ID</em>, and specifies how the previous column should be interpreted. </li> </ol> <p>The rest of the columns include the estimation results for each one of the algorithms included in the evaluation of each music facet. For <strong>each algorithms two columns</strong> are reserved, the first one containing the actual <strong>estimation</strong> and the second one the <strong>confidence</strong> of this estimation (see CSV file previews below). The format of actual estimations depends on the musical task, check the description of the <a href="https://zenodo.org/deposit/2545728">corresponding ground truth dataset</a> for more information on that. The confidence value is a float number, typically in the range from 0.0 to 1.0. It can happen that one or both columns are empty for a given analysis algorithm and CSV row. This will be the case if the algorithm could not successfully produce an estimation for the audio file row corresponding to the CSV row.</p> <p>The remaining CSV file, <em>SINGLE EVENT - Estimation Results.csv</em>, has the following 4 columns:</p> <ul> <li><strong>Freesound ID</strong>: sound ID used in Freesound to identify the audio clip.</li> <li><strong>ACExtractorV2</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool (bool).</li> <li><strong>ACExtractorV2-opt</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool with optimized parameters (bool).</li> <li><strong>ACExtractorV3</strong>: single-event<em>ness</em> estimation of the algorithm included in the third version of the Audio Commons Audio Extractor tool (bool).</li> </ul> <p> </p> <p><strong>How to get the audio data</strong></p> <p>In this section we provide some notes about how to obtain the audio files corresponding to the estimation results provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these automatic annotations.</p> <ul> <li><strong>Apple Loops (APPL)</strong>: This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#appl">provided here</a>.</li> <li><strong>Carlos Vaquero Instruments Dataset (CVAQ)</strong>: This dataset includes single instrument recordings carried out by <a href="https://www.linkedin.com/in/carlosvaquero/">Carlos Vaquero</a>as part of this <a href="http://mtg.upf.edu/node/2609">master thesis</a>. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs</li> <li><strong>Freesound Loops 4k (FSL4)</strong>: This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#instructions-for-setting-up-datasets">provided here</a>.</li> <li><strong>Giant Steps Key Dataset (GSKY)</strong>: This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations <a href="https://github.com/GiantSteps/giantsteps-key-dataset">available here</a>.</li> <li><strong>Good-sounds Dataset (GSND)</strong>: This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are <a href="https://zenodo.org/record/820937#.XEYMiy2ZN25">available here</a>.</li> <li><strong>University of IOWA Musical Instrument Samples (IOWA)</strong>: This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by <a href="http://theremin.music.uiowa.edu/MIS.html">visiting this website</a>.</li> <li><strong>Mixcraft Loops (MIXL)</strong>: This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#mixl">provided here</a>.</li> <li><strong>NSynth Dataset Test and Validation sets (NSYT and NSYV)</strong>: NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is <a href="https://magenta.tensorflow.org/datasets/nsynth">available here</a>.</li> <li><strong>Philarmonia Orchestra Sound Samples Dataset (PHIL)</strong>: This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the <a href="http://www.philharmonia.co.uk/explore/sound_samples">philarmonia orchestra website</a>.</li> <li><strong>Freesound Single Events Dataset (SINGLE EVENT)</strong>: This includes a selection of Freesound audio clips representing audio signals containing either a single audio <em>event</em>or multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#getting-fsl4-by-downloading-content-from-freesound">here</a> could be followed.</li> </ul>
Relative density variations of common vole population based on index transect, Septfontaines - Le Souillot, France (1990-2000)
<p>Transects were walked from village to village along a transect line. Common vole (<em>Microtus arvalis</em>) activity indices were recorded in every ten pace interval from October 1990 to April 2000. In 2014, the geographical coordinates of each interval has been computed by spatial interpolation based on georeferenced maps. Therefore, users must be aware that individual locations of intervals are unprecise, but not the general bearing of the transect in the landscape and interval succession. See articles published for reference and more details.</p> <p>During the same time span, small mammmals (including common voles) were sampled using live-trapping, see <a href="https://doi.org/10.5281/zenodo.6997316">10.5281/zenodo.6997316</a></p> <p><strong>FILE DESCRIPTION:</strong></p> <p><a href="https://zenodo.org/record/7544358/files/db.txt?download=1">db.txt </a>index transect file</p> <ul> <li>name: transect name</li> <li>date: on eight digits, '19921014' reads 14/10/1992</li> <li>ID: interval ID = number (within a given transect at a given date)</li> <li>Habitat: (indicative) the habitat category crossed. Just mentioned when passing from one category to the other; the following intervals are assumed to belong to this habitat</li> <li>ma1: number of <em>Microtus</em> holes; A, 1-5 holes; B, 6-10 holes; C > 10 holes</li> <li>ma2: answered only if A, B, or C are defined in ma1; NA, not answered (ma1 not defined), 0, zero faeces, 1 some faeces or fresh indices (runways with grass freshly cut, etc.); 2 many faeces in heaps</li> <li>long: longitude (WGS84)</li> <li>lat: latitude (WGS84)</li> </ul> <p><a href="https://zenodo.org/record/7544358/files/StudyAreaBoundingBox.kml?download=1">StudyAreaBoundingBox.kml</a> Bounding box of the study area.</p>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
Common Raven (Corvus corax) Occupancy Survey and Habitat Selection Data in Cliff Habitat of the Central Appalachian Region, USA, 2009-2010
We identified 24 cliff sites across four states of the Central Appalachian Region of the eastern USA (Kentucky, North Carolina, Virginia, and West Virginia) with known raven occupancy at which to perform occupancy surveys for estimating detection probability and the effects of covariates. We surveyed each cliff site 2-4 times in either 2009 or 2010 and recorded time-to-first detection and time to confirmed cliff occupancy during a two-hour survey. Daily surveys were completed between 06:00 and local solar noon. During each survey, we recorded covariates, including air temperature at survey start time, cloud cover, wind speed, and day of year. We also calculated the distance of the observation point from the cliff being surveyed and the forest cover around the cliff. We also collected data thought to be pertinent for habitat selection by ravens on 26 cliffs occupied by ravens and 26 cliffs deemed unoccupied by ravens in 2010. For each cliff, we measured cliff physiographic characteristics, such as cliff length, cliff height, and occlusion by vegetation, and landscape characteristics, including percent forest and urban cover around the cliff and distances from the cliff to the nearest road and human habitation.
Activity budgets and space use for two common Pacific parrotfish (Chlorurus Sp.) at Palmyra Atoll National Wildlife Refuge
Data was collected at Palmyra Atoll National Wildlife Refuge located in the Central Pacific in 2014, and the work focuses on two species of Parrotfish, Chlorurus microrhinos and Chlorurus spilurus (formerly Cholrurus sordidus). For C. microrhinos, the project was designed to collect fine-scale spatial behavior data, focusing on territory size, species interactions, and benthic impact (i.e. feeding behavior). Focal follow data was collected by one or two observer(s), either on snorkel or SCUBA, and recording focal activity down to the second. Simultaneously, the observer would be towing a GPS that was recording a location every 5 seconds and each location was then associated with a particular behavior. Throughout this study individual fish were identifiable and successive follows were possible on individuals. For C. spilurus the focus of the project was to collect behavioral time budget data on feeding, territorial defense, and spawning behavior. The ‘Chlorurus_Area_Palmyra_2014.csv’ data only covers C. Microrhinos and gives the 95% KUD area estimation for GPS towed tracks, as well as the 95% KUD area for locations where feeding was occurring. We also report the step length between successive points for the entire follow as well as where feeding was occurring. The ‘Chlorurus_Activity_Palmyra_2014.csv’ is for both C. Microrhinos and C. spilurus and reports the start and stop time of each focal follow as well as the start and stop time of each activity. Activity descriptions can be found in the metadata and the total length and phase for each individual in our study can be found in ‘Fish_Information_Chlorurus_Data_2014.csv.
Eriophorum vaginatum leaf length 2015-2017 from 2014 common gardens established at Toolik Lake, Coldfoot, and Sagwon - Alaska
Data on Eriophorum vaginatum leaf length collected from common gardens established at Toolik Lake, Coldfoot, and Sagwon in 2014 with tussocks from Coldfoot, Toolik Lake, and Sagwon. Data collected during the growing seasons of 2015, 2016, and 2017
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.