Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “Alpine steppes”
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
On following pages: 507. Cansdale's Swamp Rat (Malacomys cansdalel); 508. Edwards's Swamp Rat (Malacomys edwards); 509. Alpine Field Mouse (Apodemus alpicola), 510. Long-tailed Field Mouse (Apodemus sylvaticus); 511. Striped Field Mouse (Apodemus agrarius); 512. Western Broad-toothed Field Mouse (Apodemus epimelas); 513. Hyrcanian Field Mouse (Apodemus hyrcanicus); 514. Caucasus Field Mouse (Apodemus ponticus); 515. Herb Field Mouse (Apodemus uralensis); 516. Yellow-necked Field Mouse (Apodemus flavicollis); 517. Eastern Broad-toothed Field Mouse (Apodemus mystacinus), 518. Steppe Field Mouse (Apodemus witherbyi); 519. Nepalese Field Mouse (Apodemus gurkha); 520. Himalayan Field Mouse (Apodemus pallipes); 521. Kashmir Field Mouse (Apodemus rusiges); 522. Chevrier's Field Mouse (Apodemus chevrieri); 523. South China Field Mouse (Apodemus draco); 524. Large-eared Field Mouse (Apodemus latronum); 525. Taiwan Field Mouse (Apodemus semotus); 526. Korean Field Mouse (Apodemus peninsulae); 527. Small Japanese Field Mouse (Apodemus argenteus); 528. Large Japanese Field Mouse (Apodemus speciosus); 529. Okinawa Island Spiny Rat (Tokudaia muenninki); 530. Amami Spiny Rat (Tokudaiaosimensis); 531. Tokunoshima Spiny Rat (Tokudaia tokunoshimensis). in Muridae
On following pages: 507. Cansdale's Swamp Rat (Malacomys cansdalel); 508. Edwards's Swamp Rat (Malacomys edwards); 509. Alpine Field Mouse (Apodemus alpicola), 510. Long-tailed Field Mouse (Apodemus sylvaticus); 511. Striped Field Mouse (Apodemus agrarius); 512. Western Broad-toothed Field Mouse (Apodemus epimelas); 513. Hyrcanian Field Mouse (Apodemus hyrcanicus); 514. Caucasus Field Mouse (Apodemus ponticus); 515. Herb Field Mouse (Apodemus uralensis); 516. Yellow-necked Field Mouse (Apodemus flavicollis); 517. Eastern Broad-toothed Field Mouse (Apodemus mystacinus), 518. Steppe Field Mouse (Apodemus witherbyi); 519. Nepalese Field Mouse (Apodemus gurkha); 520. Himalayan Field Mouse (Apodemus pallipes); 521. Kashmir Field Mouse (Apodemus rusiges); 522. Chevrier's Field Mouse (Apodemus chevrieri); 523. South China Field Mouse (Apodemus draco); 524. Large-eared Field Mouse (Apodemus latronum); 525. Taiwan Field Mouse (Apodemus semotus); 526. Korean Field Mouse (Apodemus peninsulae); 527. Small Japanese Field Mouse (Apodemus argenteus); 528. Large Japanese Field Mouse (Apodemus speciosus); 529. Okinawa Island Spiny Rat (Tokudaia muenninki); 530. Amami Spiny Rat (Tokudaiaosimensis); 531. Tokunoshima Spiny Rat (Tokudaia tokunoshimensis).
Nitrogen addition, rather than altered precipitation, stimulates nitrous oxide emissions in an alpine steppe
<p>Anthropogenic-driven global change, including changes in atmospheric nitrogen (N) deposition and precipitation patterns, is dramatically altering N cycling in soil. How long-term N deposition, precipitation changes, and their interaction influence nitrous oxide (N<sub>2</sub>O) emissions remains unknown, especially in the alpine steppes of the Qinghai-Tibetan Plateau (QTP). To fill this knowledge gap, a platform of N addition (10 g m<sup>−2</sup> yr<sup>−1</sup>) and altered precipitation (± 50% precipitation) experiments was established in an alpine steppe of the QTP in 2013. Long-term N addition significantly increased N<sub>2</sub>O emissions. However, neither long-term alterations in precipitation nor the co-occurrence of N addition and altered precipitation significantly affected N<sub>2</sub>O emissions. These unexpected findings indicate that N<sub>2</sub>O emissions are particularly susceptible to N deposition in the alpine steppes. Our results further indicated that both biotic and abiotic properties had significant effects on N<sub>2</sub>O emissions. N<sub>2</sub>O emissions occurred mainly due to nitrification, which was dominated by ammonia-oxidizing bacteria, rather than ammonia-oxidizing archaea. Furthermore, the alterations in belowground biomass and soil temperature induced by N addition modulated N<sub>2</sub>O emissions. Overall, this study provides pivotal insights to aid the prediction of future responses of N<sub>2</sub>O emissions to long-term N deposition and precipitation changes in alpine ecosystems. The underlying microbial pathway and key predictors of N<sub>2</sub>O emissions identified in this study may also be used for future global-scale model studies.</p>
Contrasting response of the water use efficiency to precipitation changes between the alpine meadow and alpine steppe over the Tibetan Plateau
<p>The file of AlpineGrassland_GrowingSeason_WUE is dataset of the growing season water use efficiency of alpine grassland over the tibetan plateau during 1982-2014.</p>
Nitrogen addition, rather than altered precipitation, stimulates nitrous oxide emissions in an alpine steppe
Open the record for dataset details and reuse information.
Figure 2-7 from: Schmid J (2018) Remarkable discovery of the Atlanto-Mediterranean moth Scythris ventosella Chrétien, 1907 at high altitude in the Alps of Valais, Switzerland – a possible relict of the late-glacial steppe-belt fauna? (Lepidoptera, Scythrididae). Alpine Entomology 2: 45-49. https://doi.org/10.3897/alpento.2.23531
Figure 2-7 2) Biotope, 3040m a.s.l.; 3) Herniaria alpina; 4) Adult caterpillar; 5) Freshly emerged moth; 6) Reared moth. Scale bar unit: mm; 7) Wild moth. Scale bar unit: mm.
Figure 1 from: Schmid J (2018) Remarkable discovery of the Atlanto-Mediterranean moth Scythris ventosella Chrétien, 1907 at high altitude in the Alps of Valais, Switzerland – a possible relict of the late-glacial steppe-belt fauna? (Lepidoptera, Scythrididae). Alpine Entomology 2: 45-49. https://doi.org/10.3897/alpento.2.23531
Figure 1 Male genitals (above) and sternum 8 (below) of S. ventosella. Site: Switzerland, Canton of Valais, Zermatt, Unterrothorn 3040 m, 6. VIII. 2016, leg., gen. prep. and coll. Jürg Schmid.
Figure 8 from: Schmid J (2018) Remarkable discovery of the Atlanto-Mediterranean moth Scythris ventosella Chrétien, 1907 at high altitude in the Alps of Valais, Switzerland – a possible relict of the late-glacial steppe-belt fauna? (Lepidoptera, Scythrididae). Alpine Entomology 2: 45-49. https://doi.org/10.3897/alpento.2.23531
Figure 8 Currently known distribution of S. ventosella in the Alps (red asterisk) and in the Western Mediterraneum (blue dots).
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.