Find research datasets worth reusing

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-10 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-10 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-11 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-11 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-12 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-12 site automated weather station and associated soil substation at several temporal scales. Air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths. The nearest precipitation data is available from CSIS Block-13 site automated weather station located 290m distance.

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-13 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-13 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-14 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-14 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.

Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-15 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing

This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-15 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.

openCC (other)Jan 2025View details →

MCR LTER: Coral Reef: Priority effects in coral-macroalgae interactions can drive alternate community paths in the absence of top-down control, data for Adam 2022 Ecology

These data were generated in support of the manuscript: Adam TC, Holbrook SJ, Burkepile DE, Speare KE, Brooks AJ, Ladd MC, Shantz AA, Thurber RLV, and Schmitt RJ, Ecology The outcomes of species interactions can vary greatly in time and space with the outcomes of some interactions determined by priority effects. On coral reefs, benthic algae rapidly colonize the disturbed substrate. In the absence of top-down control from herbivorous fishes, these algae can inhibit the recruitment of reef-building corals, leading to a persistent phase shift to a macroalgae-dominated state. Yet, corals may also inhibit colonization by macroalgae, and thus the effects of herbivores on algal communities may be strongest following disturbances that reduce coral cover. Here, we report results from experiments conducted on the fore reef of Moorea, French Polynesia, where we: 1) tested the ability of macroalgae to invade coral-dominated and coral-depauperate communities under different levels of herbivory, 2) explored the ability of juvenile corals (Pocillopora spp.) to suppress macroalgae, and 3) quantified the direct and indirect effects of fish herbivores and corallivores on juvenile corals. We found that macroalgae proliferated when herbivory was low but only in recently disturbed communities where coral cover was also low. When coral cover was < 10%, macroalgae increased 20-fold within one year under reduced herbivory conditions relative to high herbivory controls. Yet, when coral cover was high (50%), macroalgae were suppressed irrespective of the level of herbivory despite ample space for algal colonization. Once established in communities with low herbivory and low coral cover, macroalgae suppressed recruitment of coral larvae, reducing the capacity for coral replenishment. However, when we experimentally established small juvenile corals (2 cm diameter) following a disturbance, juvenile corals inhibited macroalgae from invading local neighborhoods, even in the absence of herbivore

opencc-by-4.0Mar 2024View details →

Data from: Selective social interactions and speed-induced leadership in schooling fish

Experimental datasets for the manuscript: <div>Puy, A., Gimeno, E., Torrents, J., Bartashevich, P., Miguel, M. C., Pastor-Satorras, R., & Romanczuk, P. (2024). Selective social interactions and speed-induced leadership in schooling fish. Proceedings of the National Academy of Sciences, 121(18), e2309733121.</div> <div> </div> The datasets provide trajectories of fish. There are 2 recordings with N=39 fish (60 minutes duration) and 6 recordings with N=8 fish (30 minutes duration). The columns are as follows: <ul> <li>Time [frame]: Time of the trajectory in frames.</li> <li>X_0 [px]: Position in the x-coordinate in pixels of the trajectory of individual 0.</li> <li>Y_0 [px]: Position in the y-coordinate in pixels of the trajectory of individual 0.</li> <li>X_1 [px]: Position in the x-coordinate in pixels of the trajectory of individual 1.</li> <li>Y_1 [px]: Position in the y-coordinate in pixels of the trajectory of individual 1.</li> <li>...</li> </ul> Conversion to international units: <ul> <li>50 frames = 1 s.</li> <li>2745 px= 100 cm.</li> </ul>

opencc-by-4.0Apr 2024View details →

Raw data for PIP2 interaction with TRPC3, explored through computation and electrophysiology

The transient receptor potential canonical type 3 (TRPC3) channel plays a pivotal role in regulating neuronal excitability within the brain via its constitutive activity. The channel is intricately regulated by lipids and has previously been demonstrated to be positively modulated by PIP2. Using molecular dynamics simulations and patch clamp techniques, we reveal that PIP2 predominantly interacts with TRPC3 at the L3 lipid binding site, located at the intersection of pre-S1 and S1 helices. We propose a novel signal transduction pathway from the L3 through the re-entrant loop to a salt bridge between the TRP helix and S4-S5 linker. Notably, we find that both stimulated and constitutive TRPC3 activity require PIP2. These structural insights into the function of TRPC3 are invaluable for understanding the role of the TRPC subfamily in health and disease in native tissue.

opencc-zeroJun 2024View details →

Global Biotic Interactions: Interpreted Data Products hash://md5/e76bf914309ad27dce6ab911d8854590 hash://sha256/ba79836caab5b7ba2d7d659123d27c89f4ad990bd50f97ded935edee9fbe9f87

Global Biotic Interactions: Interpreted Data Products Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit.  Citation -------- GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects. To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI: Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005. Bias and Errors -------- As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product. To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found. This version was preceded by [7].  Contents -------- README: this file citations.csv.gz: contains data citations in a in a gzipped comma-separated values format. citations.tsv.gz: contains data citations in a gzipped tab-separated values format. datasets.csv.gz: contains list of indexed datasets in a gzipped comma-separated values format. datasets.tsv.gz: contains list of indexed datasets in a gzipped tab-separated values format. verbatim-interactions.csv.gz contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. verbatim-interactions.tsv.gz contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  interactions.csv.gz: contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. interactions.tsv.gz: contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-interactions.csv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-interactions.tsv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-verbatim-interactions.csv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  refuted-verbatim-interactions.tsv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  interactions.nq.gz: contains species interactions expressed in the resource description framework in a gzipped rdf/quads format. dwca-by-study.zip: contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension. dwca.zip: contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension. neo4j-graphdb.zip: contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data. taxonCache.tsv.gz: contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format. taxonMap.tsv.gz: describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format. References ----- [1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005. [2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557. [3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472 [4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400. [5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978. [6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955. [7] GloBI Community. (2024). Global Biotic Interactions: Interpreted Data Products hash://md5/946f7666667d60657dc89d9af8ffb909 hash://sha256/4e83d2daee05a4fa91819d58259ee58ffc5a29ec37aa7e84fd5ffbb2f92aa5b8 (0.7) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11552565 Content References ----- hash://sha256/5f4906439eba61f936b3dd7455a62c51656a74206f82d3f654e330fda6fbbe45  citations.csv.gz hash://sha256/c8100368dae39363b241472695c1ae197aaddc6e3d6c0a14f3f5ee704b37f3f6  citations.tsv.gz hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f  datasets.csv.gz hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f  datasets.tsv.gz hash://sha256/f11dc825609cdb1d4a3e9ba8caca9bf93c90dd6f660c7f6a0c8aa01c035a5e1f  dwca-by-study.zip hash://sha256/7f16aacacae74e8b0cdef04c612ba776f508ff7ffe385abc57583e37aec8fe53  dwca.zip hash://sha256/b65e4c9a3615f1386bb97e45fb907d053df55476149aa6d71e6f398351218d0d  interactions.csv.gz hash://sha256/0c28032392f82d753690be126805e6334ca46bdc4b5e2102a79b15ce0cc0ba90  interactions.nq.gz hash://sha256/8a7031250c288ba0da3d5cdbedc19d54c2f16ba3aa70d49826a7369b6edeca04  interactions.tsv.gz hash://sha256/d0c0fbf536cc63c004d057efc14600ba8cc5874f401b08f51837273b7854f1bb  neo4j-graphdb.zip hash://sha256/50e77636f8b58c040e38b6a70ba7cc8288b190ef252dc0d4eb2f12f4c541e82f  README hash://sha256/a74e2a39cfe133ae9de1eeea94f5dda8cbd58cfe61a8ccf91b7c540757719c74  refuted-interactions.csv.gz hash://sha256/37b06e274e41ca749399763989816854101238ade9863365f384a2764c639e9d  refuted-interactions.tsv.gz hash://sha256/23315b6cd3fdc91f9c1d5d5bc39fa52cf1cef7a4e97d9d023d452751df13f30e  refuted-verbatim-interactions.csv.gz hash://sha256/ff82e40cee4f8a8852d0c241f5027f66157a2b8a9090ffa3a0a329a206828d96  refuted-verbatim-interactions.tsv.gz hash://sha256/f072fbc7affb6e29978c7540af6cdccd3a219a23b0a4765b5bae56bd20df0d88  taxonCache.tsv.gz hash://sha256/cd28c81bb2432646a81ad216bc11818f7568ce81826e0074d9a33579da2c1426  taxonMap.tsv.gz hash://sha256/a1d14aa47806c624cf7e3a8c8236643dcf19ed1835c79c65958f7317ebfb9566  verbatim-interactions.csv.gz hash://sha256/2284434219d5fdab1e2152955f04363852c132b76709c330d33e31517817a82e  verbatim-interactions.tsv.gz hash://md5/d6ebf42729d988e15cb30adfa6112234  citations.csv.gz hash://md5/42877ae68e51871b8eb7116e62f6b268  citations.tsv.gz hash://md5/3e437580296fdeff3b6f35d1331db9d1  datasets.csv.gz hash://md5/3e437580296fdeff3b6f35d1331db9d1  datasets.tsv.gz hash://md5/fe88720fd992771bd64bfa220ad6a7d3  dwca-by-study.zip hash://md5/cbe132a9288feaef2f3e0c0409b8dc2f  dwca.zip hash://md5/051f6db667c4b84616223c2776464dbf  interactions.csv.gz hash://md5/b66857f8750e56ba9abe484b1f72eac4  interactions.nq.gz hash://md5/300839c346184b2fedc4e1fb31bcc29c  interactions.tsv.gz hash://md5/e79cf5ffee919672f99ea338f3661566  neo4j-graphdb.zip hash://md5/898678f47561d7ef53722bc32957dcd9  README hash://md5/65a185f19df304e53f92a7275f2de291  refuted-interactions.csv.gz hash://md5/bc37a4354f8a2402e9335ae44f28cbd7  refuted-interactions.tsv.gz hash://md5/42e817c31e2ca05e582be94e6ec283c5  refuted-verbatim-interactions.csv.gz hash://md5/93639b70a1d8e47fd194b6384c0287a7  refuted-verbatim-interactions.tsv.gz hash://md5/e32482b3697aa928a5fcb58a570191df  taxonCache.tsv.gz hash://md5/75251510925875d3fdc1952cc4b98043  taxonMap.tsv.gz hash://md5/6a0c6224f4a4c3dca9994d70ad0b2fd2  verbatim-interactions.csv.gz hash://md5/905acb49a700e5b5a292be02c917e710  verbatim-interactions.tsv.gz

opencc-by-4.0Dec 2023View details →

Data for Publication: "Automated Investigation of Metal-Ligand Interactions by a Newly Established Robotic Workflow for Titrations"

This dataset contains the whole primary and raw (original) data for the manuscript "Automated investigation of metal-ligand interactions by a newly established robotic workflow for titrations".

opencc-by-4.0Jul 2021View details →

Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"

Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields. Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample. Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.

opencc-by-4.0Dec 2021View details →

Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"

Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .

opencc-by-4.0Dec 2021View details →

Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines

Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.

opencc-by-4.0Feb 2022View details →

2019 search and interaction log from the data catalogue: Research Data Australia

In order to provide a better support to user's data discovery activity, we analysed a data search log in order to understand how data seekers interact with a data search system when they search for data.  The data search log is from the research data discovery portal: <a href="https://researchdata.edu.au">Research Data Australia (RDA)</a>. RDA  is the data discovery service of the Australian Research Data Commons (ARDC). ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program. Please read the research paper "<a href="https://doi.org/10.1108/JD-12-2021-0245">Large-scale Analysis of Query Logs to Profile Users for Dataset Search</a>" for detailed description and analysis of the datasets, and the software "<a href="https://zenodo.org/record/6321621#.Yh79Tt9xUmA">Python code for processing and clustering a data search log</a>" for the data process and analysis. The search log consists of the entire user-front activity log data for the duration of January to December 2019.  During this period, the catalogue contained about 150,000 metadata records of datasets. The dataset (2019_search_log_sessioned.txt) was generated from raw log data with following steps: <ul> <li>Remove entries that were likely from machines instead of human users. Those recorded machine activities may result from downstream aggregators who harvested metadata from RDA by directly sending queries to the catalogue URL instead of using the API endpoint.</li> <li>Identify search sessions from a user - a search session includes all activities a user conducts with a search system in order to satisfy a (information/data) search needs.  We followed the following steps to identify search sessions. First, we identified a user by IP address, where a unique IP address was considered a single user. We recognise the limitation of this approach, as several users may share the same IP address, however the IP address is the only information available for identifying a user.  Past research in log analysis usually apply the following two methods to identify a session: 30 minutes from the same IP address, and/or more than 30 minutes of inactivity between the current activity event and its immediate preceding event. We examined both methods carefully for our log data and concluded that both ended with large unwanted sessions from machine activities. Therefore, we take a brutal approach, by taking only a session from an IP address with a maximum 30 minutes duration.</li> <li>We also removed sessions whose 40% of activities resulted in ’page not found’ or whose activities were all about accessing grants. Within a session, we removed "duplicated" activities that were exactly as their precedent activity with less than one second time span (this could have been a result of reloading a page).</li> </ul> The dataset (id_to_title_subject.csv) lists title and subject headings per record id.

opencc-by-4.0Oct 2021View details →

Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)

Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen. Contents of set 2:     - Data set and materials used for the human-robot interaction experiment and for data analysis Files:     - "Questionnaire.pdf": Questionnaire used for data collection.     - "Video links.txt": Weblinks to stimuli videos used.     - "Data set.xls": Collected raw data.     - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.     - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.     - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.     - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM. The experiment procedure is described in the paper. The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201

opencc-by-4.0Sep 2021View details →

Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell"

Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell" as .npz files. The "datasets.csv" file lists, where each dataset is used in the journal publication. Additionally, there are two Python scripts (.py) which show how to load and plot a dataset. The dataset can be selected by changing the file name string in the script. Note, that for loading and plotting the simulation dataset the "plot_simulation.py" script has to be used.

opencc-by-4.0Jun 2020View details →

Learning to embed lifetime social behavior from interaction dynamics - Data

Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics" The following files are included: <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual </li> <li> rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the \(R^2\) value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window </li> <li> indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa </li> <li> time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected </li> </ul> See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.

opencc-by-4.0May 2022View details →

Data from: The interaction of ice and law in Arctic marine accessibility

Sea ice levies an impost on maritime navigability in the Arctic. But ice cover diminution due to anthropogenic climate change is generating expectations for improved accessibility in coming decades. Projections of sea ice cover retreating preferentially from the eastern Arctic suggest key provisions of international law of the sea will require revision. Specifically, protections against marine pollution in ice covered seas enshrined in Article 234 of the United Nations Convention on the Law of the Sea have been used in recent decades to extend jurisdictional competence over the Northern Sea Route only loosely associated with environmental outcomes. Projections show that plausible open water routes through international waters may be accessible by mid-century under all but the most aggressive of emissions control scenarios. While inter- and intra-annual variability places the economic viability of these routes in question for some time, the inevitability of a seasonally ice-free Arctic will be attended by a reduction of regulatory friction and a recalibration of associated legal frameworks.