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13,499 results for “researcher”
List of research data repositories that were shut down
<p>This dataset aggregates information about 191 research data repositories that were shut down. The data collection was based on the registry of research data repositories re3data and a comprehensive content analysis of repository websites and related materials. Documented in the dataset are the period in which a repository was active, the risks resulting in its shutdown, and the repositories taking over custody of the data after.</p>
Data Artifact: Rebasing Microarchitectural Research with Industry Traces
<p>Data Artifact of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the original CVP-1 traces used in the paper.</p><p><i>Note</i>: the improved converted traces used in the paper are available at https://doi.org/10.5281/zenodo.10199624.</p><p><i>Abstract</i>: Microarchitecture research relies on performance models with various degrees of accuracy and speed. In the past few years, one such model, ChampSim, has started to gain significant traction by coupling ease of use with a reasonable level of detail and simulation speed. At the same time, datacenter class workloads, which are not trivial to set up and benchmark, have become easier to study via the release of hundreds of industry traces following the first Championship Value Prediction (CVP-1) in 2018. A tool was quickly created to port the CVP-1 traces to the ChampSim format, which, as a result, have been used in many recent works. We revisit this conversion tool and find that several key aspects of the CVP-1 traces are not preserved by the conversion. We therefore propose an improved converter that addresses most conversion issues as well as patches known limitations of the CVP-1 traces themselves. We evaluate the impact of our changes on two commits of ChampSim, with one used for the first Instruction Championship Prefetching (IPC-1) in 2020. We find that the performance variation stemming from higher accuracy conversion is significant.</p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Data and scripts for collective intelligence research (arXiv:2204.13424)
<p>This is the data and scripts for the study <strong>From Prediction Markets to Interpretable Collective Intelligence</strong> by Alexey V. Osipov and Nikolay N. Osipov (<a href="http://doi.org/10.48550/arXiv.2204.13424">arXiv:2204.13424</a> [cs.GT])</p>
Cognition in Social Engineering Empirical Research: a Systematic Literature Review
<p># Description of contents</p> <p>This repository contains the codebook, dataset and analysis scripts in R used for the following publication: Pavlo Burda, Luca Allodi, Nicola Zannone. "Cognition in Social Engineering Empirical Research: a Systematic Literature Review". ACM Transactions on Computer-Human Interaction (TOCHI).<br> The repository consists of the following files:</p> <p>## dataset_and_codebook.xlsx contains the dataset, the codebook and a detailed description of contents.</p> <p>## scripts/ contains the R scripts used for the analysis</p> <p>## readme.txt contains this readme</p> <p><br> # Dataset and codebook</p> <p>The dataset_and_codebook.xlsx contains the following sheets:</p> <p>## Codebook<br> Contains a detailed description of the dataset (tables, columns, fields, etc.).<br> The codebook describes the concepts and variables that are present in the dataset. This includes explanations on meaning, numerical values, classification schemes and labels.</p> <p>## Hypotheses table <br> Contains the hypotheses for all analyzed papers and is used in the results of the paper. <br> Each row is a hypothesis of an included paper, cells contain one or more values (e.g., value1,value2,...) with or without sub values (e.g., value1,value2(sub-value1, ...)). Any content that is in square brackets [] is ignored in the analysis. Empty cells mean that there is no applicable value for that column.</p> <p>## Papers table<br> Contains the analyzed papers and is used in the results of the paper. It is also used in the overview table (Table 6) in Appendix C.<br> Each row is an included paper, cells contain up to two values (e.g., value1, value2) or the word 'multiple' in case of more than two values. Empty cells mean that there is no applicable value for that column.</p> <p>## Values table <br> Contains the description of cell values of 'Hypotheses' and 'Papers' and sub-values (specific variables in a study) that belong to a value. <br> It has a hierarchical structure from left to right where each field on the right column falls under the first non-empty field on the immediate left-top.</p> <p><br> # Reproducing results with scripts/ (generate figures)<br> To run the R scripts included in the 'scripts' directory it is sufficient to follow the instructions in and run the 'RUN_ALL_SCRIPTS.R' in 'scripts' directory.<br> The scripts use the TSV (Tab Separated Values) format of the dataset, which is the exact copy of the 'Papers' and 'Hypotheses' tables in the 'dataset_and_codebook.xlsx' file.<br> The resulting figures are stored in the 'scripts/results' directory.</p>
Machine Learning Applications in Marketing: Literature Review and Research Agenda
<p>Currently, machine learning applications in marketing allow to optimize strategies, personalize experiences and improve decision making. However, there are still several research gaps, so the objective is to examine the research trends in the use of machine learning in marketing. A bibliometric analysis is proposed to assess the current scientific activity, following the parameters established by PRISMA-2020. Machine learning applications in marketing have experienced steady growth and increased attention in the academic community. Key references, such as Miklosik and Evans, and prominent journals, such as IEEE Access and Journal of Business Research, have been identified. A thematic evolution towards big data and digital marketing is observed, and thematic clusters such as "digital marketing", "interpretation", "prediction", and "healthcare" stand out. These findings demonstrate the continued importance and research potential of this evolving field.</p>
Long Term Ecological Research (LTER) Network Site Boundaries
This is a set of Long Term Ecological Research (LTER) site boundaries preserved as a shapefile. Note that for some sites “real” boundaries are included while others–particularly marine sites–use a simpler bounding box method. Each site is listed both with its three-letter abbreviation and its unabbreviated name. World Geodetic System 1984 (WGS84) is the coordinate reference system used (EPSG:4326). More information about any of the sites can be found on the LTER Site Profiles page on the LTER Network website (https://lternet.edu/site). This data product was made possible by the contributions of the Information Managers for the LTER sites, each of whom contributed their sites' boundaries. This version of the data includes the following changes from the prior version: - Arctic (ARC) updated - Cedar Creek (CDR) updated - Florida Coastal Everglades (FCE) updated - Harvard Forest (HFR) updated - Konza Prairie (KNZ) updated - McMurdo Dry Valleys (MCM) updated - Moorea Coral Reef (MCR) updated
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from February through December 2016. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2017 through December 2017. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Aboveground plant biomass, 2009-2017. (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/275/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/501/17. The abstract below was extracted from the Level 0 data package and is included for context: The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes aboveground plant biomass from winter warming, summer warming, and control treatment plots at CiPEHR.
Vascular plant list on the Andrews Experimental Forest and nearby Research Natural Areas, 1958 to 1979
This list compiles all plant taxa which have been encountered in the the H.J. Andrews Experimental Forest, the 6000-hectare Lookout Creek drainage in the western Oregon Cascades. Habitat and abundance are described for most taxa.
Ecological Forestry in Western Oregon: A Critical Analysis from Andrews Forest LTER Research, 2014-2015
This work highlights the normative dimensions of “ecological forestry,” a strategy of forest management that uses silviculture to mimic the effects of non-anthropogenic processes of disturbance and succession in order to meet multiple objectives on a single piece of land. An analysis of the arguments made about ecological forestry, both broadly theoretical and pertaining specifically to western Oregon, shows that empirical uncertainties and normative gaps need to be addressed before we can make a clear, well-reasoned decision about whether ecological forestry is a viable and appropriate strategy for forest management and conservation.
Toolik Inlet Discharge Data collected in summer 2008, Arctic LTER, Toolik Research Station, Alaska.
Stream discharge, stage height, temperature, and conductivity of Toolik Inlet during the 2008 study season.
Toolik Inlet Discharge Data collected in summer 2007, Arctic LTER, Toolik Research Station, Alaska.
Stream discharge, stage height, temperature, and conductivity of Toolik Inlet during the 2007 study season.
Meteorological data collected on Toolik Lake during the ice free season since 1989 to 2009, Arctic LTER, Toolik Research Station, Alaska.
Yearly file describing the metological conditions on Toolik Lake (named the Toolik Lake Climate station), adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This is a floating climate station and should not be confused with the Toolik Field Station Climate site (TFS Climate Station or Met Station) which is a terrestrial station (located on land). Note that this land station has been called the "Toolik Main Climate Station", and the station on the lake is located where the main lake sampling site is located so it has also been called the Toolik Lake Main Climate Station. Measurements include air temperature, relative humidity, wind direction, wind speed, and radiation.
Water chemistry data for various lakes near Toolik Research Station, Arctic LTER. Summer 2000 to 2009.
Decadal file describing the water chemistry in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 2000 to 2009. Chemical analyses were conducted on samples from various depths in the sample lakes either once, or multiple times during the spring, summer and fall months (May to September). Chemical analyses for the samples include alkalinity, dissolved organic and inorganic carbon (DIC/DOC), inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP), particulate carbon, nitrogen and phsphorous (PC, PN, PP), cations (Ca, Mg, K, Na and Si) and anions (SO4, Cl). See methods for the yearly datsets which were combined into this data set.
Water chemistry data for various lakes near Toolik Research Station, Arctic LTER. Summer 1990 to 1999.
Decadal file describing the water chemistry in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1990 to 1999. Chemical analyses were conducted on samples from various depths in the sample lakes either once, or multiple times during the spring, summer and fall months (May to September). Chemical analyses for the samples include alkalinity, dissolved organic and inorganic carbon (DIC/DOC), inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP), particulate carbon, nitrogen and phsphorous (PC, PN, PP), cations (Ca, Mg, K, Na and Si) and anions (SO4, Cl). See methods for the yearly datsets which were combined into this data set.
Water chemistry data for various lakes near Toolik Research Station, Arctic LTER. Summer 1983 to 1989.
Decadal file describing the water chemistry in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1983 to 1989. Chemical analyses were conducted on samples from various depths in the sample lakes either once, or multiple times during the spring, summer and fall months (May to September). Chemical analyses for the samples include alkalinity, dissolved organic and inorganic carbon (DIC/DOC), inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP), particulate carbon, nitrogen and phosphorous (PC, PN, PP), cations (Ca, Mg, K, Na and Si) and anions (SO4, Cl). See methods for the yearly datasets which were combined into this data set.
Toolik Inlet Discharge Data collected in summer 2005, Arctic LTER, Toolik Research Station, Alaska.
Stream discharge, stage height, temperature, and conductivity of Toolik Inlet during the 2005 study season. Water level was recorded with a Stevens PGIII Pulse Generator and water temperature and conductivity with a Campbell Scientific Model 247 Conductivity (EC) and Temperature probe. A Campbell Scientific CR510 data logger logged the data.
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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.