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8,460 results for “MONITORING”
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore Lake Fryxell (NFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore Lake Fryxell SLIME transect is located approximately 500 m west of the Lake Fryxell Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore East Lake Bonney (SELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2017-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore Lake Fryxell (SFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The South Shore Lake Fryxell SLIME transect is located approximately 1500 m west of the F6 Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Larrea Seedling Monitoring Study at the Sevilleta National Wildlife Refuge, New Mexico (1999-2018)
The germination rate of creosote (Larrea tridentata) on the Sevilleta appears to be very low. During the early years of the LTER project it was attempted to quantify such germination through the use of seedling plots which were monitored on a bi-annual basis (spring and fall). During the period from 1989 through 1992 there were no creosote seedling that germinated on the monitoring plots. In 1999 a rather sizeable population of small seedlings was observed in one quite localized area in the vicinity of Five Points. This rather large number of individuals in one very limited area raised several questions:1. When did this small community of individuals germinate- the supposition is that they had all germinated at the same time.2. What conditions existed in this particular location which allowed such prolific germination when there was no indication of virtually any other germination in this vicinty.3. The most immediate question was whether these seedlings would survive through the impending La Nina winter, spring and summer as many of the individuals did not appear to be very "healthy." To help answer these questions it was decided to begin a "small" monitoring project. All of the individuals in this small area that appeared to be of the same age were marked with posts, located with GPS, measured (height) and photographed. This initial survey included 78 individuals. The population was resurveyed in August, 2000 and it was discovered that: 1. Many individuals were missed in the initial survey; 2. Most of the individuals (72 of 78) had survived the intervening 9 months; and 3. The average individual growth during this period was 1.4 cm. It is planned that this population will continue to be monitored (probably on an annual basis) to track survival and growth rate of these individuals.
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
EPSRC HEED Data Repository: Stove Use Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed Stove Use Monitoring Systems (SUM) on clay cook stoves in Kigeme camp, Rwanda. The aim was to (a) measure and evaluate temperature profiles within stove enclosure and on the surface of stoves (b) evaluate frequency and duration of stove use. The SUM consisted of 2 sensors - a thermocouple (to measure temperature within the stove) and a Si7021 sensor (to measure temperature and humidity outside the stove), connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUM was powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.</p> <p>The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUM and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUM and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here along with the metadata.</p>
Impact on pressure in above zone monitoring interval from unintentional CO2 release from a CCS reservoir
<p>The data set was created by running dynamic numerical simulations with the CMG-software on a static geologic model. It consists of simulations of 16 different CO2 release scenarios from a subsurface storage formation and describes the change in pressure in the above zone monitoring interval over time. The data are daily means of the pressure over a period of 1001 days and are defined on a 160x160 grid. It contains data from 4 release locations where for each location it is simulated 4 different release rates.</p>
EPSRC HEED Data Repository: Lantern Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lantern is in use. These interrupts are, in turn, processed to calculate the step count of the user. Additionally, the voltage of lantern battery is measured using an in-house designed voltage monitor to evaluate the discharging and charging patterns. The updated values of step count, rate of change of steps and device and lantern battery voltage are stored only if a significant change in the step count is detected. The device is packaged within the lantern casing and powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.</p> <p>The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. Moreover, several incidents of theft and device failures were reported leading to loss of data. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to fix these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board and internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here along with the metadata.</p>
Damage Monitoring of Structural Resins Loaded with Carbon Fillers: Experimental and Theoretical Study
<p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Simulation model, Dynamic Light Scattering, FTIR spectroscopy</p> <p>Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, </p> <p>Thermal Image Camera , Water sorption measurement, Transmission Electron Microscopy, Stress Strain</p>
Data for: Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability
<p>Monitoring of a software system provides insights into its runtime behavior, improving system analysis and comprehension. System-level monitoring approaches focus, e.g., on network monitoring, providing information on externally visible system behavior. Application-level performance monitoring frameworks, such as Kieker or Dapper, allow to observe the internal application behavior, but introduce runtime overhead depending on the number of instrumentation probes.<br /> We report on how we were able to significantly reduce the runtime overhead of the Kieker monitoring framework. For achieving this optimization, we employed micro-benchmarks with a structured performance engineering approach. During optimization, we kept track of the impact on maintainability of the framework. In this paper, we discuss the emerged trade-off between performance and maintainability in this context.<br /> To the best of our knowledge, publications on monitoring frameworks provide none or only weak performance evaluations, making comparisons cumbersome. However, our micro-benchmark, presented in this paper, provides a basis for such comparisons. Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring
<p>There are two files for each subject:</p> <p>1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below).</p> <p><br> The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes</p> <p>n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment.</p> <p>datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,<br> i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400</p> <p>electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]</p>
Utrecht University Open Access Monitor: Methodology
<p>Methodology used for the Utrecht University Open Access Monitor, which attributes OA labels to peer reviewed journal articles registered by researchers in the UU CRIS and UMCU CRIS (PURE).</p>
National Open Access Monitor, Ireland - Research product metadata
<p>This dataset contains the foundational data for the National Open Access Monitor under an open license. OpenAIRE will routinely provide monthly data dumps to Zenodo, encompassing a comprehensive set of data and indicators related to the Open Access Monitor. This collaborative effort ensures accessibility and openness in sharing the data, promoting transparency and facilitating its use for research and analysis purposes.<br>The dataset comprises of the metadata of the research products metadata stored in the parquet format. The file contains two columns ("id", "xml"), the first of which is the OpenAIRE identifier of the research product and the the second the xml representation of the metadata. The schema of the metadata can be found in https://www.openaire.eu/schema/1.0/oaf-1.0.xsd and a detailed description of the contents can be found in https://graph.openaire.eu/docs/ and https://zenodo.org/records/2643199</p>
Montenegro results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. </p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2022 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2021 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2020 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2019 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2018 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2017 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Open ruimte voor de monitoring van patrijs in Vlaanderen
<p>Deze kaartlaag omvat de relevante open ruimte voor patrijs (<em>Perdix perdix</em>) in Vlaanderen. <a href="https://doi.org/10.5281/zenodo.5814813">https://doi.org/10.5281/zenodo.5814813</a> bevat de broncode en basisdata die aan de oorsprong liggen van deze kaartlaag.</p>
NAPLES (moNitoring mAps of camPania voLcanoES)
<p>NAPLES is an open access database. You can view and download the Vesuvius Observatory monitoring networks maps, grouped by monitoring network and geographic areas. The active networks are:<br>• Seismic Network<br> o Permanent Seismic Network<br> o Mobile Seismic Network<br>• Geodetic Network<br> o GPS network<br> o Tiltmetric network<br> o Mareographic Network<br> o Altimetric Network<br> o Gravimetric Network<br> • Volcanological Network<br> o Permanent Network of Thermal Cameras<br> o Periodic Campaigns - Thermal Monitoring with Mobile Thermal Camera and Thermocouple<br>• Geochemical Network<br> o Permanent Geochemical Network<br> o Periodic Campaigns</p>
MOSID (Microcontroller On-chip Sensor IDentification): A dataset of readings from the internal monitoring sensors of STM32L152RTXX microcontrollers during the stimulation of their electronic activity
<p>The MOSID (Microcontroller On-chip Sensor IDentification) dataset consists of 5 acquired data subsets (6,72 GB total, compressed into 560 MB), each collected during different experiments and periods using various equipment (HMP4040, DF1731SB & HM305) and acquisition strategies. These subsets contain readings from the temperature and voltage sensors embedded in 20 STM32L-DISCOVERY devices. The data was captured during the execution of 5 different workloads as stimuli, repeated over 20 iterations. The stimuli employed are as follows:</p><ol><li>20x20 Long-type matrix product.</li><li>20x20 Float-type matrix product.</li><li>Algorithm for ascending sorting, Bubble Sort.</li><li>Algorithm for 2D-point clustering, Convex Hull.</li><li>Encryption algorithm AES 128-bit.</li></ol><p>The subsets are structured according to the folder format "X_Y," where X is the manually assigned number to the board, and Y is the corresponding number for the executed algorithm. Within each of these folders, files are present in the format "data_Z.txt," where Z represents the iteration number to which the file belongs. In total, the dataset comprises 9600 files with a final size of approximately 7 GB. The different presented subsets are as follows:</p><ul><li>ACQ1: Derived from the experiment named "Automatic Acquisition 1 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ2: Derived from the experiment named "Automatic Acquisition 2 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ3: Derived from the experiment named "Individual Acquisitions (HMP4040)", performed board by board from idle conditions (20 out of 20 boards, 2000 files).</li><li>ACQ4: Derived from the experiment named "GOLD SOURCE DF1731SB Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards excluding boards , 1800 files).</li><li>ACQ5: Derived from the experiment named "HANMATEK HM305 Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards, 1800 files).</li></ul><p>In each "data_Z.txt" file, starting from the 5th line, temperature and voltage raw ADC conversions from the sensors are provided, captured during the execution of the stimulus in successive lines. Additionally, a table (Table_UIDS.csv) with metadata for each of the boards used in the experiments is included, which is needed in order to normalize the data in terms of ºC and Volts.</p><ul><li>BOARD_NUM, which contains the manually assigned board number.</li><li>UID, which contains the Unique Identifier of the board assigned by the manufacturer.</li><li>T_CAL_1, which holds the calibration value of the board's temperature sensor at 30ºC.</li><li>T_CAL_2, which holds the calibration value of the board's temperature sensor at 100ºC.</li><li>VREFINT_CAL, which contains the calibration value of the board's voltage sensor.</li></ul>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
<p>Contained within this directory is the latest dataset utilized in the research titled 'ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems'. We are providing a downloadable ZIP file that includes the survey questionnaire, the amassed data, and the Jupyter Notebooks utilized for our analytical process.</p>
National Open Access Monitor Survey: Organisational Identity: Responses Dataset
<p>This dataset contains the response data from the 'National Open Access Monitor Survey: Organisational Identity which was carried out between 9th October and 9th November 2023 under the National Open Access Monitor Project: <a href="https://zenodo.org/doi/10.5281/zenodo.8420404">https://zenodo.org/doi/10.5281/zenodo.8420404</a></p><p><strong>This survey was run to:</strong></p><ul><li>compile a complete list of Irish research performing organisations (RPO) and research funding organisations (RFO) to be represented by the National Open Access Monitor.</li><li>ensure each organisation/entity is categorised correctly as an RPO or an RFO (or both), as applicable.</li><li>identify if an organisation/entity is publicly funded.</li><li>identify formally affiliated organisations/entities where <strong>all </strong>research outputs of one organisation/entity should also be included in the research outputs of another organisation/entity.</li><li>capture the persistent identifiers for RPOs, RFOs and publishers, to enable identification of relevant research outputs for the National Open Access Monitor.</li></ul><p><strong>To note: </strong></p><ul><li>Respondents' email addresses have been redacted.</li><li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor A, Research Funding Organisation A, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a>.</li><li>Respondents were notified of the limits of pseudonymisation for this survey. Due to the nature and purpose of this survey on Organisational Identity, the identity of the organisation/entity could not be pseudonymised. Participants were advised only to participate if they were happy to do so under these conditions.</li><li>Survey responses were deleted by request of certain respondents. These responses are not included in these files.</li><li>Survey respondents were enabled to update their submissions. These changes are captured in the <i>NationalOpenAccessMonitorSurvey.OrganisationalIdentity.MasterChangeFile.</i></li></ul><p><strong>There are four files within in this dataset:</strong></p><p>- <i>Results.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.README - </i>this PDF details the changes made to the raw data, as specified in the bullet points above and a description of the files within the dataset.</p><p>- <i>Results.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.Pseudonymised.101123</i> - this is the original raw data, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised and redacted.</p><p>- <i>NationalOpenAccessMonitorSurvey.OrganisationalIdentity.MasterChangeFile.101123 </i>- this is a change file, in csv format, which documents the changes which participants requested to be made to their submissions after they were received.</p><p>- R<i>esults.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.Pseudonymised.Updated.101123 - </i>this is the original raw data, pseudonymised and redacted, with the requested changes implemented.</p><p><strong>Notes for data use:</strong></p><ul><li>The "affiliated organisations/entities" section of the survey was insufficiently described in the survey text. One-to-one follow-up and support was provided to clarify to respondents that questions 12 and 13 of the survey intended to identify where <strong>all </strong>research outputs of one organisation/entity should appear on that organisation's/entities' own RPO or RFO dashboard within the National Open Access Monitor, and <strong>also </strong>on the dashboard of another organisation/entity. </li><li>Survey respondents were notified in the introduction section to the "affiliated organisations/entities" section that "<strong>only where all entities agree there is a formal relationship in place</strong> that should be reflected in the Monitor will the link be implemented". Therefore, for the National Open Access Monitor project, only where both parties have asserted that <strong>all </strong>research outputs of one organisation/entity should also appear on the dashboard of another organisation/entity, will it be considered validated and implemented. </li><li>The geographic scope of the National Open Access Monitor Project is the Republic of Ireland. Where respondent's RPO or RFO organisations/entities are based outside the Republic of Ireland, or where respondents stated affiliations with overseas organisations/entities, they will not be actioned or implemented. To note: the survey invited responses from international <i>publishers</i> to enable filtering functionality by publisher within the National Open Access Monitor, these responses are <i>not </i>out of scope. </li></ul><p>-----------------------</p><p>The context for the survey is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a>, the National Open Access Monitor Advisory Group Meeting Minutes, 18th September 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.8405472">https://zenodo.org/doi/10.5281/zenodo.8405472</a> and the Developing the National Open Access Monitor, Ireland: Stakeholder Webinar, 22nd September 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.8370578">https://zenodo.org/doi/10.5281/zenodo.8370578.</a></p><p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/ </a><a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
ScienceDex guides
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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.