Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
69
datasets available to search
ShareScore release 0.9.0
Dataset results
69 results for “open field”
NMRduino: A modular, open-source, low-field magnetic resonance platform
<p>The NMRduino is a compact, cost-effective, sub-MHz NMR spectrometer that utilizes readily available open-source hardware and software components. One of its aims is to simplify the processes of instrument setup and data acquisition control to make experimental NMR spectroscopy accessible to a broader audience. In this introductory paper, the key features and potential applications of NMRduino are described to highlight its versatility both for research and education.</p>
Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University
Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Open field: </em>20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network.<em> </em></p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
dataset for paper "Activation energy for pore opening in lipid membranes under an electric field"
<p>Dataset for the paper "Electropermeabilization of hydroperoxidized lipid membranes".</p> <p>Data was generated from Orbit Mini miniaturized bilayer workstation (Nanion Technologies, Munich, Germany), with an inserted microelectrode cavity array (MECA 4) recording chip (Ionera Technologies, Freiburg, Germany).</p> <p>The data files have format .abf, a standard format for electrophysiological data. <br> It can be read by applications such as for instance</p> <p>- Clampex and ClampFit, from the patch-clamp software suite pCLAMP, <br> - Elements Data Analyzer, associated with the elements data reader software from Elements-IC, </p> <p><br> or imported into Python through the package pyABF 2.3.5.</p> <p>import pyabf // abf=pyabf.ABF(path+"/"+f+"/"+abffile) // data = np.vstack((abf.sweepX, abf.data)) </p> <p>Data is organized in five folders named according to target hydroperoxidation degrees:<br> POPC<br> POPC-OOH 25%<br> POPC-OOH 50%<br> POPC-OOH 75%<br> POPC-OOH 100%</p> <p>Inside each of the five above files data is organized by date, and informed with the actual measured hydroperoxidation degree for a given sample. </p>
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits.
Fig. 3 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 3. Number of infested fruits and presence of weevil larvae in different jalapeño plant parts (means ± SE). Number of infested fruits in 5 directions (A), in 3 layers (C), and at 5 spacings (E). Number of larval A. eugenii within infested fruits in 5 directions (B), in 3 layers (D), and at 5 spacings (F). Different letters indicate significant differences among the treatments (means separated by Tukey's HSD, P <0.05).
Fig. 4 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 4. Fruit wall thickness and single weight in different jalapeño plant parts (means ± SE). Fruit wall thickness (A) and single weight (B) in 5 directions, fruit wall thickness (C) and single weight (D) in 3 layers, fruit wall thickness (E) and single weight (F) at 5 spacings. Different letters indicate significant differences among the treatments (means separated by Tukey's HSD, P <0.05).
Fig. 2 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 2. Infestation and larval density of pepper weevil in 2017. Vertical bars are standard errors of the means.
Fig. 5 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 5. Relationship between the infestation level and the fruit wall thickness (A) and single weight (B). Each data point represents the number of infested fruits per plant in each fruit wall thickness or weight (means ± SE). Line was fitted using linear regression analysis.
Fig. 1 in Influence of plant direction, layer, and spacing on the infestation levels of Anthonomus eugenii (Coleoptera: Curculionidae) in open jalapeño pepper fields in North Florida
Fig. 1. (a) Adult pepper weevil feeding on the stalk of a pepper fruit; (b) young and full grown larvae inside a pepper fruit; (c) pupa inside the fruit; and (d) adult weevil exit holes in pepper fruits.
Fig. 1 in Effects of irrigation method on pollination and pollinators (Hymenoptera: Apoidea) in an open-field tomato crop
Fig. 1. Fruit set (A) and weight of fruit (B) in relation to type of irrigation and type of pollination. OM = open + mechanical pollination.
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
Raw data behavior in olfactory tubing maze and open field
Open the record for dataset details and reuse information.
Opening the museum's vault: Historical field records preserve reliable ecological data
<p><span>Museum specimens have long served as foundational data sources for ecological, evolutionary, and environmental research. Continued reimagining of museum collections is now also generating new types of data associated with, but beyond physical specimens, a concept known as "extended specimens". Field notes penned by generations of naturalists contain first-hand ecological observations associated with museum collections and comprise a form of extended specimens with the potential to provide novel ecological data spanning broad geographic and temporal scales. Despite their data-yielding potential, however, field notes remain underutilized in research due to their heterogeneous, unstandardized, and qualitative nature. We introduce an approach for transforming descriptive ecological notes into quantitative data suitable for statistical analysis. Tests with simulated and real-world published data show that field notes and our transformation approach retain reliable quantitative ecological information under a range of sample sizes and evolutionary scenarios. Unlocking the wealth of data contained within field records could facilitate investigations into the ecology of clades whose diversity, distribution, or other demographic features present challenges to traditional ecological studies, improve our understanding of long-term environmental and evolutionary change, and enhance predictions of future change.</span></p>
FIGURE 3 in Evaluation of three pesticides against phytophagous mites and their impact on phytoseiid predators in an eggplant open-field
FIGURE 3: General mean abundance per eggplant leaf (± SE) of Tetranychus urticae (a), Phytoseilus persimilis (b), Polyphagotarsonemus latus (c) and other phytoseiid species (d) in control, fenbutatin oxide (F.O.), acetamiprid (aceta.) and deltamethrin (delta.) treatments.
FIGURE 4 in Evaluation of three pesticides against phytophagous mites and their impact on phytoseiid predators in an eggplant open-field
FIGURE 4: Mean abundance per eggplant leaf (± SE) of Tetranychus urticae (continuous line) and Phytoseiulus persimilis (dotted line) observed during experiments in control (a) treated with water (gray arrows), fenbutatin oxide (b), acetamiprid (c) and deltamethrin (d) (black arrows).
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.