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6,467 results for “process”
IReL Survey Data: Article Processing Charges
<p>This is the raw data from a survey carried out from 16 October to 13 December 2019 and distributed to researchers, staff and students at the nine <a href="https://irel.ie/">IReL</a> member institutions: Dublin City University, Maynooth University, National University of Ireland Galway, Royal College of Surgeons in Ireland, Trinity College Dublin, Technological University Dublin, University College Cork, University College Dublin and University of Limerick.</p> <p>Responses were anonymised at point of capture and participants were informed that "Anonymised data will be incorporated into IReL reporting for future strategic planning and may also feed into future reporting and publications on IReL activities to illustrate issues and points."</p> <p>Records 1, 2, and 170 were removed by request, originating from institutional testing.</p> <p>The preprint analysis of this survey data is available at <a href="http://10.5281/zenodo.3891073">10.5281/zenodo.3891073</a>.</p>
FIGURE 5 in A new remarkable Isoperla species with abdominal processes from Hubei Province of Central China (Plecoptera: Perlodidae)
FIGURE 5. Isoperla sextuberculata sp. nov., male paraprocts.
Ecosystem process rates across SAFE project forest quality gradient
<b>Description: </b><p>Examining rates at which vertebrate and invertebrate functional groups carry out 3 key ecosystem functions (seed removal, invertebrate predation and scavenging) and whether this changes across the SAFE project forest quality gradient. At second order sites (tree plots) within the SAFE project landscape, bait stations to test these three processes were set up. These consisted of 10 seeds (to test seed removal), live mealworm (to test invertebrate predation) or dead mealworm (to test scavenging). 3 bait stations for each ecosystem process set up in a triangle at the third order point (insect point). These consisted of baits attached to a plastic leaf. Each of these bait stations was assigned vertebrate exclusion treatment (wire cage), invertebrate exclusion (insect glue) or control (no exclusion). Data obtained from LIDAR used as a proxy for forest quality. This should help unravel how ecosystem process rates and the dominant functional group carrying out these processes may change as forests are degraded.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><b>MRes Tropical Forest Ecology Field Course</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3908118">here</a></p><p><b>Files: </b>This consists of 1 file: Ecosystem_Process_Rates.xlsx</p><p><b>Ecosystem_Process_Rates.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Rates of Ecosystem Processes</b> (described in worksheet Ecosystem-Process-Rates)</p><p>Description: Results from ecosystem process rates experiments (described above)</p><p>Number of fields: 29</p><p>Number of data rows: 2039</p><p>Fields: </p><ul><li><b>ID</b>: Location of sampling point (Field type: location)</li><li><b>ec5_uuid</b>: Entry code on epicollect (Field type: id)</li><li><b>created_at</b>: Date and Time (Field type: datetime)</li><li><b>created_by</b>: Author email (Field type: id)</li><li><b>title</b>: Title of entry on epicollect (Field type: id)</li><li><b>X2_Block</b>: Site ID (Field type: id)</li><li><b>X3_Tree_plot_ID</b>: Tree plot number (Field type: id)</li><li><b>X4_Picture_of_tree_pl</b>: Photo of tree plot tag (Field type: id)</li><li><b>X5_Insect_point_ID</b>: Insect point number (Field type: id)</li><li><b>X6_Picture_of_insect_</b>: Photo of insect point tag (Field type: id)</li><li><b>X7_Repeat</b>: Repeat (Field type: replicate)</li><li><b>X8_Treatment</b>: Treatment categories (Field type: categorical)</li><li><b>X9_Date</b>: Date (Field type: date)</li><li><b>X10_Time</b>: Time (Field type: time)</li><li><b>X11_Ecosystem_Process</b>: Ecosystem process (Field type: categorical)</li><li><b>X13_Seeds_removed</b>: Number of seeds removed (Field type: numeric)</li><li><b>X14_Seeds_partially_e</b>: Number of seeds partially eaten (Field type: numeric)</li><li><b>X15_Seeds_untouched</b>: Number of seeds untouched (Field type: numeric)</li><li><b>X17_Live_mealworm</b>: Level of consumption of live mealworm (Field type: categorical)</li><li><b>X19_Dead_mealworm</b>: Level of consumption of dead mealworm (Field type: categorical)</li><li><b>X20_Condition_of_trap</b>: Condition of trap (Field type: categorical)</li><li><b>lat_22_GPS</b>: NA (Field type: latitude)</li><li><b>long_22_GPS</b>: NA (Field type: longitude)</li><li><b>accuracy_22_GPS</b>: GPS accuracy level (Field type: numeric)</li><li><b>X23_Observer_initials</b>: Observer identity (Field type: id)</li><li><b>X24_Photo_of_plastic_</b>: Photo of bait station - photos stored online at Epicollect5 (Field type: id)</li><li><b>X25_Comments</b>: Any additional comments (Field type: comments)</li><li><b>Dead_mealworm_binary</b>: Dead mealworm presence (Field type: categorical)</li><li><b>Live_mealworm_binary</b>: Live mealworm presence (Field type: categorical)</li></ul></li></ol><p><b>Date range: </b>2019-02-04 to 2019-02-21</p><p><b>Latitudinal extent: </b>4.6876 to 4.7494</p><p><b>Longitudinal extent: </b>116.9558 to 117.6197</p>
Dataset: Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning
<p>Supplementary Data for <em><strong>Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning</strong></em><em><strong> </strong></em>article</p> <p>Dataset associated with the following publication:</p> <p>Maurer, L. K., Joch, M., Hegele, M., Maurer, H. & Müller, H. (2019). Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning. <em>Journal of Vision</em>, <em>19(4):18</em>, 1-13. https://doi.org/10.1167/19.4.18</p>
Data for the parameterization of radiative transfer processes in urban climate models
<p><em>Radiative Transfer</em> <em>Model</em> (RTM) is a key component in microscale building resolving urban climate models (<em>UCM</em>), which are used to simulate the flow within urban area. We use different parameterizations of RTMs in the model system <a href="https://gmd.copernicus.org/articles/13/1335/2020/gmd-13-1335-2020.html">PALM</a> version 6.0 to show how much detail modellers should include in their simulation.</p> <p>We introduce the output PALM model results for two examples: (1) A simplified urban geometry consisting of an urban crossing (UC) and (2) a realistic urban domain located at the town square Ernst-Reuter-Platz in Charlottenburg in Berlin (ER). The netCDF files contain the radiative flux received by each surface in the domains, including the shortwave (direct and diffuse) radiation as well as the longwave radiation. Also, the data set includes the 3D flow variables (<em>u</em>, <em>v</em>, <em>w</em>) and the potential temperature. The model drivers (input data) for both examples are included as well.</p> <p>The data set consists of the following model input/output data:</p> <p>1) Simplified urban domain (UC):</p> <ul> <li>Input driver for the model PALM for UC (UC_model_driver.tar.gz)</li> <li>Radiation fluxes for UC when using RTM_01: radiation for horizontal surfaces (UC_RTM_01.nc)</li> <li>Radiation fluxes for UC when using RTM_02: sky view effect (building shadows) (UC_RTM_02.nc)</li> <li>Radiation fluxes for UC when using RTM_03: vegetation interaction with SW radiation (UC_RTM_03.nc)</li> <li>Radiation fluxes for UC when using RTM_04: receiving radiation from surface emission (UC_RTM_04.nc)</li> <li>Radiation fluxes for UC when using RTM_05: vegetation interaction with LW radiation (UC_RTM_05.nc)</li> <li>Radiation fluxes for UC when using RTM_06: single reflection (UC_RTM_06.nc)</li> <li>Radiation fluxes for UC when using RTM_07: vegetation interaction with reflected radiation (UC_RTM_07.nc)</li> <li>Radiation fluxes for UC when using RTM_08: multiple reflections (UC_RTM_08.nc)</li> <li>3D data for the UC reference case which includes u,v,w,theta</li> </ul> <p>2) Realistic urban domain (ER):</p> <ul> <li>Input driver for the model PALM for ER (ER_model_driver)</li> <li>Radiation fluxes for ER when using RTM_01: radiation for horizontal surfaces (ER_RTM_01.nc)</li> <li>Radiation fluxes for ER when using RTM_02: sky view effect (building shadows) (ER_RTM_02.nc)</li> <li>Radiation fluxes for ER when using RTM_03: vegetation interaction with SW radiation (ER_RTM_03.nc)</li> <li>Radiation fluxes for ER when using RTM_04: receiving radiation from surface emission (ER_RTM_04.nc)</li> <li>Radiation fluxes for ER when using RTM_05: vegetation interaction with LW radiation (ER_RTM_05.nc)</li> <li>Radiation fluxes for ER when using RTM_06: single reflection (ER_RTM_06.nc)</li> <li>Radiation fluxes for ER when using RTM_07: vegetation interaction with reflected radiation (ER_RTM_07.nc)</li> <li>Radiation fluxes for ER when using RTM_08: multiple reflections (ER_RTM_08.nc)</li> <li>3D data for the ER reference case which includes u,v,w,theta</li> </ul> <p>For more information and analysis, please check out the relevant publication in the international journal Geoscientific Model Development: Salim et. al, Importance of radiative transfer processes in urban climate models:A study based on the PALM model system 6.0, submitted to GMD.</p>
Supplementary material 1 from: Milanović M, Knapp S, Pyšek P, Kühn I (2020) Trait–environment relationships of plant species at different stages of the introduction process. NeoBiota 58: 55-74. https://doi.org/10.3897/neobiota.58.51655
Tables S1, S2
Figure 1d from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1d A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Fossilised animal skin (Natural History Museum 2009)
Figure 1b from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1b A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Pinned insect specimen (Natural History Museum 2018)
Figure 1c from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1c A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Microscope slide (Natural History Museum 2017)
Figure 1a from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1a A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Herbarium specimen (Natural History Museum 2007a)
Figure 11 from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 11 The distribution of languages across the specimen and herbaria. EN=English, FR=French, LA=Latin, ET=Estonian, DE=German, NL=Dutch, PT=Portuguese, ES=Spanish, SV=Swedish, RU=Russian, FI=Finnish, IT=Italian, ZZ=Unknown. The codes for the contributing herbaria are listed in Table 11 (from Dillen et al. 2019).
Supplementary material 1 from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Appendices
Figure 1e from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1e A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Liquid preserved specimen (Natural History Museum 2010)
Shaft Misalignment Data for: Inter-component Phase Processing of Quasipolyharmonic Signals
<p>This dataset contains observations of vibration of test stand in three states: normal state, horizontal and vertical parallel misalignment. Full dataset is avaliable at:</p> <p>https://data.mendeley.com/datasets/pt9mjcvghd/1</p>
Composite 2D video of raw and processed video footage from an outdoor camerawork training session for qualitative data collection
<p>The video clip, in traditional 2D format, contains a short excerpt (1:43 minutes) from an outdoor camerawork training session. The raw and processed footage from the different cameras is composited in a single frame. The original composite file has 32 channels of audio so that one can switch between different microphones and combinations of microphones. This is indicated in the video clip but is not available in this file. All participants are playing particular roles in the training session, and each carries a camera. In preparation for the real data collection with a guide, one person is pretending to be a nature guide. She carries a GoPro camera on a gimbal. There is an instructor, who is carrying a single lens 360° camera on a raised extension pole with a separate ambisonic microphone. Two others are filming with a prosumer camcorder and a single lens 360° camera on a lowered extension pole respectively. And a fifth person is filming with a stereoscopic 360° camera and an independent ambisonic microphone on a monopod. In a nutshell, this is a typical team filming arrangement, in which the team needs to attentively yet silently coordinate their joint camerawork. Languages: Danish and English</p>
Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process-data
<p>This file is the data used in the aritcle "Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process"</p>
Data associated with the publication titled The impact of resolving sub-kilometer processes on aerosol-cloud interactions in global model simulations
<p>Datasets and scripts that are used in the journal article The impact of resolving sub-kilometer processes on aerosol-cloud interactions in global model simulations</p>
Customer Service - Device Repair Process (sp2020)
<p>This event log is from a service process of a home appliances vendor for repairing faulty devices.</p> <p><strong>Attributes</strong></p> <ul> <li>CASE_ID: Unique Case Identifier of the Service order</li> <li>ACTIVITY: Label of the process step (see description of process)</li> <li>TIMESTAMP: Time when the activity was finished</li> <li>REPAIR_IN_TIME_5D: Flag that indicates if the service level was met (i.e. repair completed within 5 days)</li> <li>DEVICETYPE: Unique identifier for each product (the first two letter indicate the device type)</li> <li>SERVICEPOINT: Unique identifier for the service partner responsible for the repair</li> </ul> <p> </p> <p><strong>Description of the process activities</strong></p> <ul> <li>Creation: A service order has been created by the hotline or on-site at the service counter</li> <li>Letter: The letter to the customer has been printed</li> <li>DeviceReceived: Parcel with the customer's device has been received</li> <li>StatusRequest: Customer has requested a status update (by phone)</li> <li>Completed: Repair of the device has been completed and it is ready for shipping</li> <li>Transmission: Estimate of the repair costs has been sent to the customer</li> <li>Approved: Cost estimate has been approved by the customer</li> <li>NoteHotline: The hotline has made a remark in the service order</li> <li>NoteWorkshop: The workshop has made a remark in the service order </li> <li>StockEntry: Used spare parts have been booked as taken from the warehouse</li> <li>InDelivery: Device has been sent to customer</li> <li>Freeticket Cust/ Freeticket Comp: can be ignored/legacy</li> </ul>
Cleaned and pre-processed MS/MS datset (build from all positive ionmode spectra in GNPS) - zip file
<p>Large MS/MS dataset build from data that was obtained from GNPS (accessed on 2020-05-11): <a href="https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.json">https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.json</a></p> <p>The data was cleaned and pre-processed using notebooks provided here: https://github.com/iomega/spec2vec_gnps_data_analysis/tree/master/notebooks</p> <ul> <li>112,956 positive ionmode spectra</li> <li>metadata was cleaned and corrected using matchms (https://github.com/matchms/matchms) and lookup routines using PubChem</li> <li>92,954 of the spectra have Smiles and InchiKey (13717 unique InchiKey in first 14 characters)</li> </ul> <p> </p> <p>Was used for the main article on Spec2Vec --> <a href="https://doi.org/10.1371/journal.pcbi.1008724">https://doi.org/10.1371/journal.pcbi.1008724</a></p>
Dataset for the paper: "The Gas Accessible Membrane Electrode (GAME): A Versatile Platform for Elucidating Electrocatalytic Processes Using Real Time and In Situ Hyphenated Electrochemical Techniques"
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors: Guohui Zhang, Anthony Kucernak</p> <p>Title: The Gas Accessible Membrane Electrode (GAME): A Versatile Platform for Elucidating Electrocatalytic Processes Using Real Time and In Situ Hyphenated Electrochemical Techniques </p> <p>Journal: ACS Catalysis, 2020</p> <p>DOI: 10.1021/acscatal.0c02433</p> <p>Please cite the above reference if you wish to use this data<br> DOI of data:10.5281/zenodo.3985811</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.