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431 results for “register”
Figure. The study area where the species were registered (localities: 1–15). in The herpetofauna of the Great Ulcinj Beach area including Ada Island (Montenegro)
Figure. The study area where the species were registered (localities: 1–15).
A Review of Candidate Interaction Records in Biodiversity Datasets registered with GBIF and iDigBio as seen via Biodiversity Interaction Finder (BIF) hash://sha256/56ce90e39c2f3a038d577faad70b65573e7ab2e0e69f735f14d71cf4b3b0966b hash://md5/6c497d45da7f79d054d8a5203bd439c0
<p>This is a work in progress. Please check back later for updates.</p> <p> </p> <p> </p>
World Register of Marine Species
The aim of a World Register of Marine Species (WoRMS) is to provide an authoritative and comprehensive list of names of marine organisms, including information on synonymy. WoRMS data published to EOL include taxon names and classification, images and Notes, and attribute data including Environment and Distribution.<p></p>For permission to access this resource, please visit <p></p>http://www.marinespecies.org/usersrequest.php <p></p>Please contact the WoRMS Maintainer with any questions
Arctic Biodiversity: Arctic Register of Marine Species
Biogeography and other attributes for Arctic organisms, various sources.<p></p>Sirenko, B.I.; Clarke, C.; Hopcroft, R.R.; Huettmann, F.; Bluhm, B.A.; Gradinger, R. (eds) (2016). The Arctic Register of Marine Species (ARMS) compiled by the Arctic Ocean Diversity (ArcOD). Accessed at <p></p>http://www.marinespecies.org/arms on 2016-06-03
Arctic Biodiversity: Arctic Register of Marine Species
Biogeography and other attributes for Arctic organisms, various sources.<p></p>Sirenko, B.I.; Clarke, C.; Hopcroft, R.R.; Huettmann, F.; Bluhm, B.A.; Gradinger, R. (eds) (2016). The Arctic Register of Marine Species (ARMS) compiled by the Arctic Ocean Diversity (ArcOD). Accessed at <p></p>http://www.marinespecies.org/arms on 2016-06-03
Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson's disease patients
<p>MER data consist of brain signal in different depths when DBS surgery is being done. In each depth, a data file is created, with different duration depending on the depth, and up to three channels.</p> <p>Data come from 14 patients (9 males and 5 females), they are anonymized and labelled from P1 to P14. They correspond to patients in age 65.1 +- 5.6 years.</p> <p>Data are organized in STN-IN and STN-OUT (different depths in each folder), subthalamus-in, and subthalamus-out since the STN area is the target area when implanting a DBS. Classification in STN-IN and STN-OUT was made by the neurophysiologists and surgeons.</p> <p>Data were recorded for left and right lobes, 8 patients in left and right lobe, 1 patient in right lobe, and 5 patients in left lobe.</p> <p>The format is mat file (MATLAB file)</p> <p>Data sampling frequency is 12kHz.</p> <p>No filtering or data processing was made, they are directly obtained from the MER acquisition system.</p> <p> </p>
German Renewable Energy Unit Register
<p>The UnitRegister dataset is a comprehensive compilation of all renewable energy units connected to the grid in Germany. It combines data from two primary sources: the <a href="https://www.marktstammdatenregister.de/MaStR/Datendownload">Marktstammdatenregister</a> published by Germany's Federal Network Agency (BNetzA), and individual publications from the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Anlagenstammdaten">webpage</a>.<br> The dataset encompasses active, decommissioned, and planned units, resulting from the merging and cleaning of these two unit registers. The Marktstammdatenregister dataset is regularly updated and was last downloaded in December 2022. The TSOs' unit register is updated annually around August, with a one-year lag, and the last data extraction took place in 2022, providing information until 2021.<br> While both datasets are similar, the main difference lies in the reporting format. The TSOs report units as bundles of installations under a common payment code (Unit_ID), whereas BNetzA publishes each installation as a separate unit with an individual identification code (BNetzA_Unit_ID). For example, a wind park with multiple turbines is reported as a single unit by the TSOs, whereas BNetzA provides information on each turbine separately.<br> The main dataset, "UnitRegister.csv," is located in the "3Output" folder. It comprises more than 2.4 million units categorized into ten technologies, including Geothermal, Hydropower, Gas_landfill, Gas_mine, Gas_sewage, Biomass, Wind_on, Wind_off, Solar_GM, and Solar_RM. This dataset consists of 13 columns/variables, including "Unit_ID," "BNetzA_EEG_ID," "BNetzA_Unit_ID," "Technology," "Capacity_kW," "Commissioning," "Decommissioning," "State," "PLZ," "Source," "RefYield," "Depth," and "Coast_dist."<br> In addition to the main dataset, this publication provides separate files for different stages of data processing. For example, "UnitRegister_TSOs.csv" and "UnitRegister_BNetzA.csv" offer clean datasets for the TSOs and BNetzA sources individually. The file "UnitRegister_BNetzA_Raw.csv" contains the compiled BNetzA register in its raw form, comprising 82 columns/variables. Detailed information on these variables can be found in the BNetzA user manual, which is included in the "Dokumentation MaStR Gesamtdatenexport" folder within the BNetzA directory.</p> <p>Notes:</p> <p>The dataset has undergone merging and cleaning processes aligned with the research objectives of the Chair of Energy Economics at BTU. It is important to note that these objectives may differ from other research purposes. Specifically, our research focused on analyzing payments associated with renewable energy projects, giving priority to the TSO data. The BNetzA data set was used to supplement the unit stock and fill in data gaps for TSO units whenever possible.<br> Users should exercise caution when selecting the data source that best aligns with their research goals. To assist in this decision-making process, we have included the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 22), users can run the code. However, it is crucial to verify that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past) and update the file reading sections in the code accordingly.</p> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. To gain a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:<br> • Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p>
German Renewable Energy Tariff Register
<p>The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">webpage</a>. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs.</p> <p>Notes:</p> <p>The TariffRegister.csv can be matched with the <a href="https://zenodo.org/record/8010410">PaymentRegister</a> dataset to identify the payment types received by renewable energy units.</p> <p>The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors.</p> <p>Users can update the dataset by following the outlined steps:</p> <ol> <li>Update the “Original files” by downloading the latest published data (<a href="http://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">here</a>)</li> <li>Clean the files from any row that is not including a Tariff_ID</li> <li>Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs.</li> <li>Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files.</li> <li>Run the provided R code after modifying the top-level directory.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <ul> <li>Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></li> <li>Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></li> </ul>
German Renewable Energy Payment Register
<p>The PaymentRegister dataset offers a comprehensive compilation of subsidies and tariffs paid to renewable energy units installed in Germany between 2000 and 2021. The dataset is constructed based on the yearly statements provided by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. These statements are updated annually around August, with a one-year lag, and the dataset encompasses yearly statements from 2007 to 2021.<br> Containing over 53 million observations, the dataset comprises 12 variables/descriptors. It includes a unit identifier (Unit_ID) that can be matched with the <a href="https://zenodo.org/deposit/7945029">UnitRegister</a> dataset, along with information on generation (G_kWh) and associated payments (P_Euro). Additionally, descriptors related to the associated tariff are provided, such as the Tariff identification code (Tariff_ID) and the regulated tariff value (Tariff). The dataset also includes the technology of the unit, a tariff categorization (Ref1, Ref2) based on the payment type (e.g., full feed-in tariff, market premium, bonus payment, reduced payment due to sanction), the year of payment (Year), and the name of the TSO under which the unit is registered (TSO). Notably, two additional descriptors, "Tariff1" and "P_Euro1," are included to highlight any discrepancies between reported tariff values and actual payments.</p> <p>Notes:</p> <p>The PaymentRegister.csv dataset was compiled to meet the research needs of the Chair of Energy Economics at BTU. Payments were categorized based on their tariffs, resulting in a more specific first categorization process with 29 categories, followed by a second categorization level grouping the tariffs into 17 categories. However, these categorizations are solely descriptive and do not impact the original data, allowing users to choose whether to utilize the categorization descriptors or not.</p> <p>To assist users in utilizing the dataset, we have provided the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 13), users can run the code. To update the dataset, the following steps are recommended:</p> <ol> <li>Update the TariffRegister following this publication.</li> <li>Download the new year statements from the TSOs into each TSO folder and run the individual TSO data analysis R-codes. Please modify the top-level directory for these codes as well and update the sections for the new data. Ensure that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past).</li> <li>Execute the main code to bind all the files together.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/deposit/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <p>• Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p> <p> </p>
Vintage Cash register from my dad's attic
Grand pa's 1920's Cash register from my dad's attic. Canon 750d, 70 images. Source: Objaverse 1.0 / Sketchfab
Study to Evaluate the Safety and Immunogenicity of Nimenrix (Registered) in Healthy Infants, Given at 3 and 12 Months of Age
ClinicalTrials.gov study NCT04819113. IPD Sharing: YES. Countries: 3. Publications: 1.
Paclitaxel and CBT-1(Registered Trademark) to Treat Solid Tumors
ClinicalTrials.gov study NCT00972205. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Inclusion Appeal Videos and Intent to Register for Pediatric Clinical Trials
ClinicalTrials.gov study NCT06138145. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Phase II Study of the Efficacy and Toxicity of Ontak(Registered Trademark) (Denileukin Diftitox) in the Therapy of Adult T-Cell Leukemia
ClinicalTrials.gov study NCT00117845. IPD Sharing: NO. Countries: 1. Publications: 3.
Clinical Evaluation of the PROGENSA(Registered Trademark) Prostate Cancer Gene 3 (PCA3) Assay in Men With a Previous Negative Biopsy Result
ClinicalTrials.gov study NCT01024959. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study To Evaluate The Efficacy Of Enbrel (REGISTERED) Etanercept Over A Period Of 12 Months In The Routine Treatment Of Patients With Rheumatoid Arthritis, Axial Spondyloarthritis, Psoriatic Arthrit
ClinicalTrials.gov study NCT02486302. IPD Sharing: NO. Countries: 1. Publications: 2.
German Ablation Quality-Register
ClinicalTrials.gov study NCT01197638. IPD Sharing: NO. Countries: 1. Publications: 2.
German Corneal Cross Linking Register
ClinicalTrials.gov study NCT00560651. IPD Sharing: Not stated. Countries: 1. Publications: 4.
The Long-term Antibody Persistence of MenACWY-TT Vaccine (PF-06866681) Versus Meningitec(Registered) or Mencevax(Registered) ACWY in Healthy Adolescents and Adults and a Booster Dose of MenACWY-TT Adm
ClinicalTrials.gov study NCT01962207. IPD Sharing: YES. Countries: 1. Publications: 1.
Implementation and Effectiveness of a Psychosocial Intervention Among Registered People Living With HIV(HIV Patients) at Pakistan Institute of Medical Sciences (PIMS) Islamabad Pakistan.
ClinicalTrials.gov study NCT06869330. IPD Sharing: NO. Countries: 1. Publications: 1.
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.