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CAP LTER weather stations at Papago Park and Lost Dutchman State Park in the greater Phoenix metropolitan area of central Arizona, USA, ongoing since 2010
The CAP LTER maintains two micrometeorological stations (10-m height) in the greater Phoenix metropolitan area, including at Lost Dutchman State Park and near the Desert Botanical Garden at Papago Park. The local terrain at both sites is flat or gently sloping Sonoran desert, and the vegetation canopy consists of patchy coverage of desert shrubs and trees. The dominant vegetation species include bursage (*Ambrosia deltoidea*) and creosote bush (*Larrea tridentata*), while minor species include palo verde (*Parkinsonia microphylla*) and saguaro cactus (*Carnegiea gigantea*). Wind speed and direction, incoming solar radiation, air temperature, relative humidity, and precipitation have been monitored nearly continuously since the fall of 2010. Each variable is measured every 5 seconds and the average (or total for precipitation and total solar radiation) saved to a data logger every 10 minutes.
Gold-Caps_LMD-Matched_General
<p>This dataset contains captions for the <a href="https://colinraffel.com/projects/lmd/">Lakh MIDI Dataset-matched</a> music dataset (~30,000 tracks with accompanying MIDI files).</p><p>These captions were generated by the <strong>gpt-4-1106-preview</strong> chat endpoint prompted to describe each track based on the track title and artist. The captions have not been filtered or post-processed in any way.</p><p><strong>Prompt used:</strong><br>"Give a general description of the track <title> by <artist_name> in one sentence. Don't mention the title or artist."</p>
Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan
<p><strong>README Grigoriev data</strong></p> <p><strong>Overview</strong></p> <p>The Grigoriev data collection consists of the following files, which are briefly explained further below.<br>From a relatively large number of files and for clarity, we provide mainly those files which have been directly<br>used in the generation of figures contained in Machguth et al. (2024). While the use in figure<br>creation was the main selection criteria, the files have not been truncated to data shown in the figures. <br>The files contain more information than shown in the figures. A few files have been added for completeness although<br>not used to create figures (see below).</p> <p>The data sets provided in this repository are listed in the following. Most of these tables contain relatively raw data. <br>The suggested citations are added in brackets. Please also check Table 1 in Machguth et al. (2024) for potential further references.</p> <p>- 1990_GRG_90_H1-BETA.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx (Arkhipov et al., 1996; Thompson et al., 1997; Machguth et al., 2024)<br>- 2001_GRG_01_S1-EE.xlsx (Arkhipov et al., 2004; Mikhalenko et al., 2005)<br>- 2001_metals.pdf (Usubaliev, 2003)<br>- 2003_GRG03-S1-EE_001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_GRG03-S2-EE 001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_pits.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_density.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_logger_data.xls (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx (Machguth et al., 2024)<br>- 2018_PSI_dating_20230517.xlsx (Eichler et al., 2020; Machguth et al., 2024)</p> <p><br><strong>Detailed Information</strong></p> <p>1990_GRG_90_H1-BETA.xlsx: refers to Core 1 1990 (labelled H1 probably for "Hole 1"). Unknown to what the 1991 data refer, likely a repeat measurement.</p> <p>1990_GRG_90_H1-CHM.xlsx: Chemistry Core 1 1990.</p> <p>1990_GRG_90_H1-STRAT.xlsx: Stratigraphic information Core 1 1990</p> <p>1990_GRG_90_H2-CHM.xlsx: Chemistry Core 2 1990</p> <p>1990_GRG_90_H2-STRAT.xlsx: Stratigraphic information Core 2 1990</p> <p>1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx: This table we calculated from the 1990 tables as well as the 2018 data for the purpose of visualizing<br> decadal means in MIs (Fig. 7). Decadal dating of the 1990 cores was done based on the bomb horizon of 1963 (Thompson et al., 1993, 1997), <br> decadal picks from Thompson et al. (1993) and personal communication by Lonnie Thompson (email 19 June 2023).</p> <p>2001_GRG_01_S1-EE.xlsx: 2001 core, stable water isotope ratios, firn temperatures, percentage of infiltration ice, stratigraphy</p> <p>2003_GRG03-S1-EE_001.xlsx: 2003 51m and 22.6m cores, 51m core was drilled thermally, 22.6m core mechanically. For the latter similar data as for 2001</p> <p>2003_GRG03-S2-EE 001.xlsx: 2003 21.3m core. Reduced amount of measured parameters compared to e.g. 2001 core. </p> <p>2003_pits.xlsx: Stratigraphy and density measured in a series of snow pits in 2003.</p> <p>2003_temperature_density.xlsx: Density and temperature measured in 2003 22.6m core. Comparison of T_ice at 4440 m a.s.l. to 1962 core (Dikikh, 1965)</p> <p>2003_temperature_logger_data.xls: Firn temperatures measured through a thermistor chain during 3 days in June 2003. Data from 14 June have been used for Fig. 5.</p> <p>2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx: 2018 core stratigraphy and density. This is a somwhat outdated file which shows the data per centimetre.<br> The file compares the two measurements of density (only the one from the laboratory was used in Machguth et al., 2024). <br> Also contains visually observed dust layers (not shown in Machguth et al., 2024)</p> <p>2018_PSI_dating_20230517.xlsx: Complete data from the analysis of the 2018 core.</p> <p><br><strong>Bibliography</strong></p> <p>Arkhipov, S. M., Mikhalenko, V. N., & Thompson, L. (1996). Struktura i stratigrafiya deyatel’nogo sloya lednika Grigor’eva na Tyan’-Shanye (Structure and stratigraphy of the active layer <br>of the Griroriev glacier in the Tjan-Shan). Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 80, 68–83.</p> <p>Arkhipov, S. M., Mikhalenko, V. N., Kunakhovich, M. G., Dikikh, A. N., and Nagornov, O. V.: Termicheskiy reshim, uslovija l’doobrazovanija i akkumulatsija na lednike Grigor’eva (Tyan’-<br>Shan), v 1962–2001 gg. (Thermal regime, types of ice formation and accumulation on the Grigoriev glacier (Tien Shan), 1962–2001), Materialy Glyatsiologicheskikh Issledovaniy (Data<br>of Glaciological Studies), 96, 77–83, 2004.</p> <p>Eichler, A., Kronenberg, M., Brütsch, S., Rüthi, M., Heule, M., Schwikowski, M., et al. (2020). Chernobyl horizon in a Central Asian ice core. <br>Annual Report 2019 - Laboratory of Environmental Chemistry - PSI, 31.</p> <p>Kutuzov, S. S.: Prostranstvennie izmenenija i stroenie lednikov vnutrennogo Tyan’-Shanya za poslednie 150 let (Spatial changes and structure of the glaciers of the inner Tien Shan over the last 150<br>years), Master’s thesis, Lomonossov State University, Moskva, 2005.</p> <p>Machguth, H., Eichler, A., Schwikowski, M., Brütsch, S., Mattea, E., Kutuzov, S., et al. (2024). Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan. <br>The Cryosphere, 18(4), 1633–1646. https://doi.org/10.5194/tc-18-1633-2024</p> <p>Mikhalenko, V. N., Kutuzov, S. S., Fayzrakhmanov, F. F., Nagornov, . B., Thompson, L. G., Kunakhovich, M. G., Arkhipov, S. M., Dikikh, A. N., and Usubaliev, R.: Sokrashhenie oledenenija<br>Tyan’-Shanja v XIX – nachale XXI vv.: rezul’taty kernovoro burenija i izmerenija temperatury v skvazhinakh (Glacier recession in the Tien Shan from the XIX to the beginning of the XXI century:<br>results from ice core drilling and borehole temperature measurements), Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 98, 175–182, 2005.</p> <p>Thompson, L. G., Mosley-Thompson, E., Davis, M., Lin, P. N., Yao, T., Dyurgerov, M., & Dal, J. (1993). “Recent warming” ice core evidence from tropical ice cores with emphasis <br>on Central Asia. Global Planet. Change, 7(1–3), 145–156. https://doi.org/10.1016/0921-8181(93)90046-Q</p> <p>Thompson, L. G., Mikhalenko, V., Mosley-Thompson, E., Durgerov, M., Lin, P. N., Moskalevsky, M., et al. (1997). Ice core records of recent climatic variability: Grigoriev and It-Tish ice caps <br>in Central Tien Shan, Central Asia. Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 81, 100–109.</p> <p>Usubaliev, R. A. (2003). Khimitcheskoe zagryaznenie lednikov Tyan’-Shanya (na primere lednika Grigor’eva) (Chemical pollution of Tien Shan glaciers (on the example of Grigoriev Glacier)). <br>Izvestija Natsional’noy Akademii Nauk Kirgizskoy Respubliki (News of the National Academy of Sciences of the Kyrgyz Republic), 4, 154–160.</p>
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction"
<p>Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction'', published in Chimia 2021 75:163, doi: <a href="http://doi.org/10.2533/chimia.2021.163">10.2533/chimia.2021.163</a></p> <p>Folder names describe the type of data content.</p>
Micrometeorological conditions at the CAP LTER flux tower located in the west Phoenix, AZ, USA neighborhood of Maryvale (2010-2019)
Preface ============ USERS ARE CAUTIONED THAT THE DATA PRESENTED IN THIS DATASET ARE RAW DATA DOWNLOADED DIRECTLY FROM DATA LOGGERS; THE DATA HAVE NOT BEEN PROCESSED OR CHECKED FOR ACCURACY, COMPLETENESS OR QUALITY IN ANY CAPACITY; ANY CALCULATED VALUES DERIVE DIRECTLY FROM A DATA LOGGER. Overview ============ The CAP LTER maintains a 23-m urban eddy flux tower in the west Phoenix, AZ neighborhood of Maryvale (-112.1426 W, +33.4838 N). The tower is instrumented for long-term measurement of the covariance of turbulent eddy and radiative fluxes for the purposes of urban micrometeorological research. Additional micrometerological parameters, such as precipitation, are also measured. Select soil parameters were measured from 2012 through 2016. Eddy covariance data are collected and stored at two temporal resolutions: 10 Hz and 30-minute averages. Data presented here include only 30-minute averages. Owing to large volumes, 10-Hz data are avialable by request to the CAP LTER Information Manager (caplter.data@asu.edu).
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Connecting U.S. Supreme Court Case Information and Opinion Authorship (SCDB) to Full Case Text Data (CAP), 1791-2011
<p>This dataset was constructed to connect the rich metadata created by the Supreme Court Database (SCDB) to the Caselaw Access Project (CAP) full-text court opinion data. Since the SCDB includes only substantive opinions, it is necessarily a subset of the full range of opinions available through CAP.</p> <p>There are two parts to this data: the map connecting each SCDB ID to its corresponding CAP case number, and a more advanced (but error-prone) version in which the authorship of each opinion text identified for the case in CAP is attributed to the Justice who wrote it. Each of these data products have been hand-corrected to the best of this author's ability.</p> <p><strong>SCDB-CAP map</strong></p> <p>The SCDB->CAP map began as a relatively straightforward automated matching process, based on the US Reports citation for each case as expressed in both SCDB and CAP. Slightly over 80% of SCDB entries found a single CAP data match this way. From there, the data was entirely hand-corrected, with non-matches or duplicate matches individually investigated and manually corrected.</p> <p>Some SCDB entries simply could not be matched to an appropriate CAP text. Initially, the entirety of US Reports volume 44 was missing, but with the help of CAP staff, the volume was located as having been filed in the New York jurisdiction rather that the United States jurisdiction. The case numbers were then added to the map, but until the volume is relocated to the United States jurisdiction, it may be necessary to also incorporate the New York jurisdiction in full text analysis so that the cases from volume 44 can be searched. 108 more missing cases are from US Reports volume 131, which was a "catch up" volume published in the 19th century. These catch-up cases, many heard by the Supreme Court decades prior, were numbered with lowercase roman numerals instead of the ordinary numbers, which is almost certainly why CAP's software dismissed the catch-up section as prefatory material. Many of the rest of the errors seem largely to be examples where the SCDB project recognized a separate court action that CAP did not. Perhaps most of these seem to have been later rehearings for a case previously decided, which in the 19th century particularly were commonly reported out at the end of the first decision text. While SCDB sometimes gave these subsequent but related actions a separate SCDB entry, CAP seems to have largely incorporated them as part of the text of the main case. Additionally, there were a few that simply could not be found, despite a careful look through each database as well as the original US Reports and sometimes adjacent volumes. Finally, the cases were only matched up through the 2011 court term. After the 2011 term, the mismatches between CAP and SCDB were extensive and frequently seemed impossible to resolve.</p> <p>Even so, with the manual correction, the overall error rate is low. Of 28,304 cases, only 191 do not have a match, and of those, 108 are contained within the vol. 131 "catch up" volume. Since most of the rest are extremely short subsequent actions that were separately noted by SCDB, the effect of these non-matched cases would seem to be small in most cases.</p> <p>The typical use case would be that the researcher would generate some kind of results based on searching in the CAP full text, then could use the CAP ID to look up the SCDB ID in the map. With the SCDB ID, of course, the rich metadata from the SCDB can then be connected to each result as needed.</p> <p><strong>Opinion authorship</strong></p> <p>Being able to use the rich metadata of SCDB in conjunction with a case's full text is exciting, but it immediately prompts a further question -- what if the texts could be attributed directly to the Justices who authored them? SCDB produces its data in two forms; one is "case centered," where each record represents one case, and the other is "justice centered," in which each record is the vote of one Justice in one case. CAP, in turn, breaks the total text of the case into distinct opinions, and tries to attribute those opinions to their authors by scraping a string of text from the raw input. Therefore, the challenge was to connect these two sources at the opinion level.</p> <p>Connecting the opinions, like connecting the cases, involved an initial match by machines, followed by manual correction and revision. In this case, the scope of the manual effort was much larger than that posed by the case-level connection, and more errors were noted in both SCDB and CAP.</p> <p>The matching process involved a number of steps. First a list of opinions was generated from the CAP data, then matched to SCDB using the SCDB-CAP connector data described above. (Thus, a case without a CAP match in the SCDB-CAP data will not appear in the opinion author data either.) CAP opinions were numbered in the order they were encountered in each CAP case JSON object, and these numbers are used to distinguish the opinions.</p> <p>Next, a round of automatic matching was performed. If there was only one opinion, and only one author listed in the SCDB data, then the majority opinion author (as listed in SCDB) was safely assumed to be the author. If there was no author listed in SCDB, "percuriam" was recorded as the author in this data. If there were exactly two opinions and two authors, the process was also straightforward, as the SCDB-identified majority opinion author was assigned to opinion 1, and the remaining author assigned opinion 2.</p> <p>Subsequently, cases with more than two opinions were processed. A potential match (i.e. a "guess") for each opinion in a given case was created by listing each Justice identified by SCDB as having written an opinion in the case. These guesses were then parsed using a semi-automatic procedure with Levenshtein distance fuzzy name matching. With sufficiently conservative parameters, a successful fuzzy match meant that the non-successful guesses for that opinion could be deleted. These sorted guesses were then reviewed manually. Particular care was also taken for any opinion that contained authored opinions by Justices who had similar names (for example, Clark and Black differ by only a single letter). These sorts of cases, as well as instances of co-authorship, were identified and fixed manually.</p> <p>Those opinions whose authorship could not be matched then were fixed by hand. These included some where the CAP author strings were more complicated than SCDB's strict interpretation; others where the OCR in CAP which contained the Justice name was especially bad; and a number of others where "Mr. Chief Justice" couldn't be directly matched with an author name by the machine. After this light manual correction, almost 500 opinions with substantial errors remained to be individually investigated in depth, by examining the CAP record, the SCDB record, and images of the US Reports for that case. For these last tough customers, errors in the source data were commonly the cause of matching problems. Typically these were of three kinds: examples where CAP should have split the text but didn't (e.g. 2 opinions together in one opinion entry in CAP); examples where SCDB either did not identify or mis-identified an author (such as attributing it to Swayne when it was written by Miller); and examples of non-valid opinions (such as where CAP mistakenly split the opinion too early, leaving an opinion fragment).</p> <p>For these errors, a system of codes was created in the author field to signal the error type so that researchers can be suitably cautious. The error code is always at the beginning of the field and is followed by a comma and the names of each author, separated by a comma with no space to facilitate parsing. Note also that co-authors are listed as comma-separated names in this same field with no error code. Researchers will probably want to disaggregate this field to create duplicate records with each individual author for most purposes. The justice number field also contains information about all justices authoring the opinion but the error codes have been omitted here.</p> <ul> <li>!C -- error: multiple opinion texts combined (i.e. CAP splitting error)</li> <li>!X -- error: unattributed or misattributed opinion (not listed in SCDB as writer)</li> <li>!D -- error: extra opinion that should be deleted, i.e. not a valid opinion</li> <li>!W -- error: listed as Writers by SCDB, but should be co-authors</li> </ul> <p> </p> <p><strong>Data file structure</strong></p> <p>"scdb_cap-051820.tsv" is a Tab-separated data file containing 5 columns: SCDB ID, CAP ID, US Reports citation, case date, and case name (the latter three from the SCDB data).</p> <p>"scdb-cap-opinion-authorship_051920.tsv" is a Tab-separated data file containing seven columns: SCDB ID, CAP ID, US Reports citation, case name, opinion number in the case, opinion author, and SCDB justice ID. See above for caveats about disaggregating and error codes in fields six and seven.</p> <p><strong>Errors</strong></p> <p>It is likely that errors remain in this data, and it is also hoped that some of the errors beyond the author's immediate control might be fixed in the upstream data so that they can be corrected here. Authors would be grateful for error reports, and also reports of errors fixed, if any.</p> <p> </p>
Deliverable D-IA.2.2.OH-Harmony-Cap.2.1: Completed Pilot Survey
<p>This is a public deliverable of One Health EJP Joint Research Project,<strong><em> </em></strong><strong><em>Integrative Action-2.2,</em></strong> <strong>OH-HARMONY-CAP: </strong>One Health Harmonisation of Protocols for the Detection of Foodborne Pathogens and AMR Determinants. <a href="https://onehealthejp.eu/jip-oh-harmony-cap/"><strong>https://onehealthejp.eu/jip-oh-harmony-cap/</strong></a></p> <p>The purpose is to develop an integrated One Health map (OHLabCap ) of the levels of system capability/capacity/interoperability for each of the EU MS that is repeatable and sustainable. The first step is to develop of a pilot survey, targeting NRLs and the primary diagnostic services (primary sector). The pilot survey covers: 1. six priority bacteria and ten priority parasites have been chosen, as model organisms, together with the antimicrobial resistance (AMR) testing of <em>Salmonella</em> and <em>Campylobacter</em>. 2. 63 questions incorporating capability, capacity, and interoperability</p>
Ice thickness and bed topography of all Scandinavian glaciers and ice caps
<p>Files showing the ice thickness (m) and subglacial bed elevation (m) for all Scandinavian (i.e. Norwegian and Swedish) glaciers and ice caps. Coordinate system is epsg:25833</p> <p>Related publication which should be referenced when this data is used is </p> <div> <div> <div>Frank T, van Pelt W. Ice volume and thickness of all Scandinavian glaciers and ice caps. <em>Journal of Glaciology</em>. Published online 2024:1-34. doi:10.1017/jog.2024.25 <div> </div> </div> </div> </div>
El Niño Enhances Snowline Rise and Ice Loss on the Quelccaya Ice Cap, Peru
<p>El Niño Enhances Snowline Rise on the Quelccaya Ice Cap, Peru (in-review)</p> <p>Kara A. Lamantia, Laura J. Larocca, Lonnie G. Thompson, Bryan Mark</p> <p>Exported results from automated snow cover area detection on the Quelccaya Ice Cap (QIC). Further calculated results are detailed in the supplementary documentation in the draft manuscript. See READ ME.txt file for details</p> <p>Sample Code available for Landsat 8 imagery here at the following URL: https://code.<br>earthengine.google.com/cfcbd0780ff3f09b0698035cd6dd678a</p>
Underwater images collected by an Autonomous Surface Vehicle in Cap-La-Houssaye, Réunion - 2023-06-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-La-Houssaye, Réunion - 2023-06-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 32.54 GB of MP4 files, which were trimmed into 10391 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 77.41% of these extracted images are useful and 22.59% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 88.84 %, Q2: 10.72 %, Q5: 0.43 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 58.7 GB of MP4 files, which were trimmed into 13073 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 76.51% of these extracted images are useful and 23.49% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 88.38 %, Q2: 10.85 %, Q5: 0.76 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.628 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 22.61 GB of MP4 files, which were trimmed into 8035 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 56.91% of these extracted images are useful and 43.09% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 52.3 %, Q2: 46.01 %, Q5: 1.69 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.613 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
FADN data on the support under the CAP delimited for LAU2 (NUTS2) regions in the EU Member States for the 2007-2013 programming period
<p> </p> <p>Ready to use FADN dataset on the support under the CAP in 2007-2013 delimited for LAU2 (NUTS2) regions in the EU Member States.</p> <p>Investigation of the interaction between Cohesion and Rural Policies requires analysing comparable data. However, the CAP data are usually collected at the national level. The FADN database is the only data source for analysing the impact of agricultural policy instruments on the economic situation of farms. However, the regional breakdown of FADN data in some countries does not correspond to the NUTS2 breakdown for which cohesion policy is defined.</p> <p>The provided FADN data delimitation uses a methodology that takes into account the range of impact and features specific to a given region. Because the research shows a very strong relationship between the amount of support under the CAP and the number and size of farms on a given area, this criterion was used to delimit FADN data for particular LAU2 (NUTS2) regions, while maintaining the allocation to individual measures.</p> <p>FADN data aggregated (averaged) to the level of FADN regions and economic size classes were used. Each FADN region has been assigned a corresponding NUTS2 region (or regions) according to the classification in 2010 in which the full census of the farm structure survey was carried out. The delimitation of FADN data to NUTS2 regions was based on weights constructed on the basis of Eurostat data on utilised agricultural area and number of holdings in 2010. In each economic size class, each FADN region consisted of the sum of the NUTS2 regions weighted by the utilised agricultural area. The result of each FADN variable was the sum of its values in each economic size class, weighted by the total number of holdings in each class.</p> <p>This database has served as a basis for two articles, one validating the assumptions of the NUTS2 (LAU) delimitation of the FADN regions and the other using the database to compare synergies and trade-offs between cohesion policy and the common agricultural policy.</p>
Supplemental data for "The possible transition from glacial surge to ice stream on Vavilov Ice Cap"
<p>Data presented in the paper "The possible transition from glacial surge to ice stream on Vavilov Ice Cap".</p>
Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".
<p>Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".</p> <p>David Rutherford1, Markéta Šlapal Bařinková1, Thaiskang Jamatia2, Pavol Šuly2, Martin Cvek2, Bohuslav Rezek1</p> <p><br>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16227 Prague, Czech Republic<br>2 Centre of Polymer systems, Tomas Bata University in Zlin, Trida T. Bati 5678, 760 01 Zlín, Czech Republic</p> <p><br>Dataset description:</p> <p>240909 UV-vis_capped_ZnO.xlsx UV-vis spectroscopy<br>220623 ZnO Zlin Zn ion.xlsx Zinc ion measurement<br>230511 dls_zeta_data.xlsx DLS & zeta potential measurement<br>230221 ZnO_Zlin_MIC_MASTER.xlsx Minimum inhibitory concentration</p>
Point-count bird censusing: bird abundance and diversity in CAP LTER Phoenix Area Social Survey neighborhoods throughout the greater Phoenix metropolitan area, 2006-2016
The Phoenix Area Social Survey (PASS) parallels the Ecological Survey of Central Arizona (formerly, Survey 200) as a long-term monitoring program of the CAP LTER. Every five years, the PASS research team surveys households in selected neighborhoods in the metropolitan Phoenix area to better understand perceptions, values, and behaviors of several key environmental issues, including water conservation, urban growth, air pollution, land conservation, biodiversity and urban climate change, as well as perceptions about their neighborhoods. The survey was piloted in 2001-2002 in eight neighborhoods in Phoenix with 302 respondents, and grew to over 40 neighborhoods and 800 households in 2005. Bird survey locations were established in each of the PASS neighborhoods, colocated as much as possible with the corresponding ESCA survey location in the neighborhood. Bird surveys were conducted biannualy (spring, winter) approximately the year of and the year after each PASS. In a given season, each bird survey location is visited independently by three birders who count all birds seen or heard within a 15-minute window.
Figures 16-19 in Roncus elbulli (Arachnida, Pseudoscorpiones), a new species from Cap de Creus Nature Park (Catalonia, Spain), with a key to the Spanish species of the genus Roncus
Figures 16-19. Roncus elbulli sp. n., female paratype, Cala Canadell. SEM photographs: 16. left palp, dorsal view; 17. chelal microsetae pattern below trichobothria eb/esb; 18. fingers of the chela, antiaxial face, partial view, showing trichobothrium sb and sensilla p1 and p2 on movable finger. Roncus cadinensis Zaragoza, 2007, male paratype. SEM photograph: 19. chelal microsetae pattern below trichobothria eb/ esb. Scale bars (mm): 0.05 (Figs 17, 18), 0.10 (Fig. 19), 0.50 (Fig. 16).
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.