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18,621 results for “development;”
Dielectric-loss spectroscopy development and iridium photoredox catalyst ion pairing
<p>This data accompanies the publishing of the manuscript of reviving dielectric-loss spectroscopy and ion pair reorganization in an iridium photoredox catalyst. The data consists of microwave transients, microwave resonances, absorption/emission, and Stern-Volmer quenching data.</p>
Dataset Klussmeier et al., Secretin receptor as a target in gastrointestinal cancer: expression analysis and ligand development
<p>Numerical dataset for the manuscript by Klussmeier et al., Secretin receptor as a target in gastrointestinal cancer: expression analysis and ligand development.</p>
Investigating the role of miRNA in neural crest development and diseases
<p>This is a recording of the poster presentation of Mr Marco Antonaci (UEA) at the Swiss RNA Workshop 2022, January 21st</p>
Development of thrips barcode database and multiplex real-time PCR assay for quarantine and agriculture pest species
<p>Thrips (Order Thysanoptera) species are agriculturally important as plant sap sucking pests and vectors of several plant diseases. They are very small insects and commonly associated with imported commodities at New Zealand border in all life stages. Morphological identification of thrips is mainly performed on adults, but the available identification keys for immature stages do not include many species and are inadequate, thus DNA barcode was regularly used for thrips identification, here, we have generated DNA barcode data for over 29 thrips species from over 100 individuals. At New Zealand border,<em> Frankliniella occidentalis </em>is the dominant species intercepted, followed by <em>F. panamensis</em>, <em>Thrips palmi</em> and <em>T. tabaci </em>and several other thrips species. Hence, we have also developed a multiplex real time PCR assay, targeting the four thrips species to facilitate the identification of quarantine interceptions with more accurate and faster diagnostic method for any developmental stages. The DNA barcode database further assists in thrip identification. The assay showed high specificity for all the four target species and could detect 10 copies/ µL of the target DNA. Linear responses and high correlation coefficients between the amount of DNA and <em>C</em><sub>q</sub> values for each species were also achieved. The method was tested on single egg, larva and adult and proved to be applicable for all life stages of the four species. This study has demonstrated the assay is a useful biosecurity tool for rapid and reliable identification of the target thrips species. </p>
Gamification in Software Engineering: The Mediating Role of Developer Engagement and Job Satisfaction
<p>Replication package with covariance matrices (instead of original dataset) and R script.</p>
Defining fatty acids changes linked to rumen development, weaning and growth within Holstein-Friesian heifers
<p>After birth, as effectively monogastric animals, calves undergo substantial physiological changes to become ruminants by 3 months of age and reach sexual maturity at approximately 15 months of age. Herein, we assess longitudinal metabolomic changes in Holstein-Friesian (HF) heifers from birth until sexual maturity during this developmental process. Sera from 20 healthy, HF heifers were sampled biweekly from 2 weeks of age until 13 months of age and then monthly until 19 months of age. Sera were assessed using flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer for high throughput, sensitive, non-targeted metabolite fingerprinting. Partial least squares-discriminate analysis (PLS-DA) and unsupervised hierarchical clustering analysis (HCA) of the derived metabolomes indicated changes detectable in heifers’ sera over time. Time series analyses identified 30 metabolites that could be related to rumen development and weaning at ~ 3 months of age. Further time series analysis identified 40 metabolites that could be correlated to growth. These findings highlight the role of acetic acid and 3-PP within rumen development and growth, suggest that weaning induces elevated levels of fatty acyls in response to a post-weaning stress induced innate immune response and demonstrate the utilisation of fatty acyls in growth. The identified metabolites offer serum metabolites which could inform the nutrition and healthy development of heifers</p>
Mining Fork-Including Software Development Traces
<p>This dataset relates to the paper: Mining Fork-Including Development Traces (abstract below)<br> Authors: Iris Reinhartz-Berger and Amir Tomer<br> Starting point: readme.txt</p> <p>Open-source software development is a common practice that encourages collaborative development and reuse across projects. Forking is a way to make a copy of an existing project and explore it for different purposes. Two types of forks are commonly mentioned in the literature: <em>contributing forks</em> which continue the development lines of the forked projects and aim at merging the contribution back to the forked projects; and <em>independently developed forks</em> which open new lines of development deviating from the forked projects. In this study, we aim to explore characteristics of fork-involving software development traces. Analyzing 880 Java projects and their related action and observation events, with process mining and statistical techniques, we found that the occurrence of certain event types may predict the fork type, while the creation of certain fork types increase the involvement of users in the forked projects.</p>
DCASE 2022 Challenge Task 2 Development Dataset
<p><strong>Description</strong></p> <p>This dataset is the "development dataset" for the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring"><strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>"</a>.</p> <p>The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task:</p> <ul> <li>Fan</li> <li>Gearbox</li> <li>Bearing</li> <li>Slide rail</li> <li>ToyCar</li> <li>ToyTrain</li> <li>Valve</li> </ul> <p> </p> <p><strong>Overview of the task</strong></p> <p><strong>Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. </strong>Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial intelligence (AI)-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines. </p> <p>This task is the follow-up to DCASE 2020 Task 2 and DCASE 2021 Task 2. The task this year is to detect anomalous sounds under three main conditions:</p> <p>1. Only normal sound clips are provided as training data (i.e., unsupervised learning scenario). In real-world factories, anomalies rarely occur and are highly diverse. Therefore, exhaustive patterns of anomalous sounds are impossible to create or collect and unknown anomalous sounds that were not observed in the given training data must be detected. This condition is the same as in DCASE 2020 Task 2 and DCASE 2021 Task 2.</p> <p>2. Factors other than anomalies change the acoustic characteristics between training and test data (i.e., domain shift). In real-world cases, operational conditions of machines or environmental noise often differ between the training and testing phases. For example, the operation speed of a conveyor can change due to seasonal demand, or environmental noise can fluctuate depending on the states of surrounding machines. This condition is the same as in DCASE 2021 Task 2.</p> <p>3. In test data, samples unaffected by domain shifts (source domain data) and those affected by domain shifts (target domain data) are mixed, and the source/target domain of each sample is not specified. Therefore, the model must detect anomalies regardless of the domain (i.e., domain generalization).</p> <p> </p> <p><strong>Definition</strong></p> <p>We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".</p> <ul> <li>"Machine type" indicates the kind of machine, which in this task is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics. Each section is dedicated to a specific type of domain shift. </li> <li>The source domain is the domain under which most of the training data and part of the test data were recorded, and the target domain is a different set of domains under which a few of the training data and part of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, SNR, etc.</li> <li>Attributes are parameters that define states of machines or types of noise. </li> </ul> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset consists of three sections for each machine type (Sections 00, 01, and 02), and each section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.</p> <p> </p> <p><strong>File names and attribute csv files</strong></p> <p>File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:</p> <p> [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...</p> <p> </p> <p><strong>Recording procedure</strong></p> <p>Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>- /dev_data <br> - /fan<br> - /train (only normal clips) <br> - /section_00_source_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_source_train_normal_0989_<attribute>.wav <br> - /section_00_target_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_target_train_normal_0009_<attribute>.wav <br> - /section_01_source_train_normal_0000_<attribute>.wav <br> - ... <br> - /section_02_target_train_normal_0009_<attribute>.wav <br> - /test <br> - /section_00_source_test_normal_0000_<attribute>.wav <br> - ... <br> - /section_00_source_test_normal_0049_<attribute>.wav <br> - /section_00_source_test_anomaly_0000_<attribute>.wav <br> - ... <br> - /section_00_source_test_anomaly_0049_<attribute>.wav <br> - /section_00_target_test_normal_0000_<attribute>.wav<br> - ... <br> - /section_00_target_test_normal_0049_<attribute>.wav <br> - /section_00_target_test_anomaly_0000_<attribute>.wav <br> - ... <br> - /section_00_target_test_anomaly_0049_<attribute>.wav <br> - /section_01_source_test_normal_0000_<attribute>.wav<br> - ... <br> - /section_02_target_test_anomaly_0049_<attribute>.wav<br> - attributes_00.csv (attribute csv for section 00)<br> - attributes_01.csv (attribute csv for section 01)<br> - attributes_02.csv (attribute csv for section 02) <br> - /gearbox (The other machine types have the same directory structure as fan.) <br> - /bearing<br> - /slider (`slider` means "slide rail")<br> - /ToyCar <br> - /ToyTrain <br> - /valve </p> <p> </p> <p><strong>Baseline system</strong></p> <p>Two baseline systems are available on the Github repository <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_ae">baseline_ae</a> and <a href="https://github.com/Kota-Dohi/dcase2022_task2_baseline_mobile_net_v2">baseline_mobile_net_v2</a>. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following three papers. </p> <ul> <li>Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [<a href="https://arxiv.org/abs/2206.05876">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
Assessing the Impact of Pest Monitoring Traps on Bombus griseocollis (Hymenoptera: Apidae) Colony Growth and Development
<p>Insect traps use visual and olfactory cues to attract target pests; however, they vary in their specificity and often unintentionally capture non-target beneficial insects (bycatch), including <em>Bombus</em>. Concerns have been expressed that bycatch may contribute to <em>Bombus</em> mortality and the consequential loss of pollination services. Here, we quantified the impact of trap captures on <em>Bombus griseocollis</em> colony growth and development by evaluating the following four treatments: colonies paired with traps, colonies paired with traps and pheromone lures, traps and pheromone lures (but no colonies), and colonies with no trap and no lure. Trap contents were collected biweekly to determine <em>B. griseocollis </em>capture rates. Colony growth and development data were collected weekly by weighing colonies and recording foraging activity. Based on microsatellite polymerase chain reaction (PCR) amplification, three <em>B. griseocollis </em>were collected from released colonies, while the remaining five were residents within the environment. Given the low number of <em>B. griseocollis </em>workers collected, any differences in colony weight change and active foraging were likely not a result of pest monitoring trap captures. However, trap captures could have a greater impact by interfering with functional diversity, colony establishment, and pollination services, emphasizing the need for additional research. Building on this research will provide a more comprehensive view of the impact of pest monitoring traps on <em>Bombus </em>populations, which could minimize risk to pollinator populations and pollination services.</p>
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor
<p>Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in <em>Sorghum bicolor</em>"</p>
Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"
<p>This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Váša et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data. </p> <p> </p>
Data_Figure1(A-D)_AKR1D1 knockout mice develop a sex dependent metabolic phenotype
<p>Data of Fig1 Pannel A-D, “AKR1D1 knockout mice develop a sex dependent metabolic phenotype”</p> <p>The Dataset contains the original figure 1 (Pannel A-D) as PNG-format (10.1530JOE-21-0280_Fig1A-D.PNG). Corresponding raw from LC-MS/MS measurements are provided as one file in CSV format (31003A-179400_10.1530_JOE-21-0280_AKR1D1_SS_DVK_4_Fig1.csv), all further experiment related information (meta-data) as one file in TXT format (31003A-179400_10.1530_JOE-21-0280_AKR1D1_SS_DVK_4_Fig1_M1.txt), one file in PDF format (31003A-179400_10.1530_JOE-21-0280_AKR1D1_SS_DVK_4_Fig1_M2.pdf) and one file as CSV format (31003A-179400_10.1530_JOE-21-0280_AKR1D1_SS_DVK_4_Fig1_M3.csv).</p>
Biodiversity conservation and socioeconomic development
<p>This data set contains the data used to analyse the synergies and trade-offs between biodiversity conservation and local socioeconomic development in and around sample African and European PAs. The data were collected based on face-to-face survey with PA managers.</p>
Fig. 3 in Observations On The Embryonic Development Of Trichuris Sylvilagi (Nematoda, Trichuridae) Under Laboratory Conditions
Fig. 3. Changes in the size of Trichuris sylvilagi eggs during embryogenesis at the temperature of 30 ° C: а — egg length; b — egg width; с — egg plug length; d — egg plug width; е — eggshell thickness (μm). А — zygote stage; В — larval stage; the small squares in the centre corresponds to the median, the lower and upper borders of the large rectangular correspond to the first and the third quartiles, respectively, vertical line segments, directed up and down from the rectangular, correspond to minimum and maximum values; * P <0.05, ** P <0.01, *** P <0.001 — relative to the parameters of eggs at the zygote stage (n = 15).
Fig. 1 in Observations On The Embryonic Development Of Trichuris Sylvilagi (Nematoda, Trichuridae) Under Laboratory Conditions
Fig. 1. Stages of embryonic development Trichuris sylvilagi: а — zygote; b — formation of two blastomeres; с — blastomere cleavage; d — bean-shaped embryo; e — tadpole-shaped embryo; f — larva.
Fig. 1 in Influence Of Temperature On Breaking Diapause, Development And Emergence Of Megachile Minutissima (Hymenoptera, Megachilidae)
Fig. 1. Percentage of newly emergence of M. minutissima males and females under incubation conditions at 30 ± 0.4 °C after different cold storage periods.
Fig. 1 in Morphological Differentiation In Nestlings Turdus Philomelos (Passeriformes, Turdidae) And Staging In Their Development During The Nestling Period Of Postembryogenesis
Fig. 1. Dynamics of changes in 12 morphometric traits in nestlings during the nesting stage of postembryogenesis (from 1 to 14 days), the stage of fast growth (from 1 to 8 days) and of slow growth (from 8 to 14 days).
Fig. 9 in Eight Species Of Anuran Amphibians (Amphibia, Anura) Found In Ukraine: Comparative Morphology And Classification Of Larval Development Stages
Fig. 9. The structure of four species' anuran amphibian's sucker at the first stage of development (view from below).
Fig. 6 in Eight Species Of Anuran Amphibians (Amphibia, Anura) Found In Ukraine: Comparative Morphology And Classification Of Larval Development Stages
Fig. 6. Stages characterizing the beginning of metamorphosis: 23 — resorption of the fin's cloacal piece; 24 — front limbs are seen through the skin.
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.