Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Unveiling the potential of redox chemistry to form size tunable, high index silicon particles

In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.

opencc-by-4.0Aug 2024View details →

Dataset on the Index of Sustainable Economic Welfare for the EU27 and beyond.

<div>This dataset contains data about two ISEWs for the EU27, its individual Member States (MS), the UK and the US. Following Van der Slycken and Bleys (2023) (1), two variants of the ISEW are presented in this dataset: the ISEW_BCE accounts for the benefits and costs of the present and pasts activities experienced in the present and within a specific country (Benefits and Costs Experienced); the ISEW_BCPA accounts for the benefits and costs of present activities experienced in the present and in the future, both domestically and internationally (Benefits and Costs of Present economic Activities).</div> <div> </div> <div>This document contains different datasets. Two datasets contain a summary of the values of the ISEWs and their components in ‘per capita’ terms. One summary presents the results for the EU27 (and MS) and the other one presents the results for the UK and the US (Non-EU countries). Additionally, each component is presented in some details in different pages, allowing to see the value of the different subcomponents included in each component (and even the value of some items with subcomponents for some components).</div> <div> </div> <div>The period covered by this dataset is 1995-2020.</div> <div> </div> <div>All the components are described in the accompanying table and in the report.</div> <div> </div> <div> </div> <div>(1) Van der Slycken, J. and Bleys, B. (2023). Towards ISEW and GPI 2.0: Dealing with Cross-Time and Cross-Boundary Issues in a Case Study for Belgium. Social Indicators Research, 168(1):557-583.</div>

opencc-by-4.0Feb 2021View details →

Last interglacial sea-level index points in the Western Mediterranean

Sea-level index points, dated samples and correlated metadata for the Western Mediterranean. This dataset was assembled in the framework of the World Atlas of Last Interglacial Shorelines. Field descriptors are available at: https://walis-help.readthedocs.io/en/latest/ See readme files for updates with respect to version 2.0

opencc-by-4.0Nov 2024View details →

30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)

Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (Vv) and bare soils (Vs) across China in 2014, 2018 and 2022. These pixel-wise Vv and Vs maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in Vv and Vs and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.

openCC (other)Jan 2020View details →

Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons

This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.

openOpenNov 2024View details →

Aquatic biofilm autotrohic index, carbon dioxide flux, and environmental conditions for the APEX water table experiment 2021-2023

To better understand linkages between hydrology and ecosystem carbon flux in northern aquatic ecosystems, we evaluated the relationship between plant communities, biofilm development, and carbon dioxide (CO2) exchange following long-term changes in hydrology in an Alaskan fen. We quantified seasonal variation in biofilm composition and CO2 exchange in response to lowered and raised water-table position (relative to a control) during years with varying levels of background dissolved organic carbon (DOC). We then used nutrient-diffusing substrates to evaluate cause-effect relationships between changes in plant subsidies (i.e., leachates) and biofilm composition among water-table treatments. We found that background DOC concentration determined whether plant subsidies promoted net autotrophy or heterotrophy on nutrient diffusing substrates. In conditions where background DOC was <= 40 mg L-1, plant subsidies promoted an autotrophic biofilm. Conversely, when background DOC concentration was >= 50 mg L-1, plant subsidies promoted heterotrophy. Greater light attenuation associated with elevated levels of DOC may have overwhelmed the stimulatory effect of nutrients on autotrophic microbes by constraining photosynthesis while simultaneously allowing heterotrophs to outcompete autotrophs for available nutrients. At the ecosystem level, conditions that favored an autotrophic biofilm resulted in net CO2 uptake among all water-table treatments, whereas the site was a net source of CO2 to the atmosphere in conditions that supported greater heterotrophy. Taken together, these findings show that hydrologic history interacts with changes in dominant plant functional groups to alter biofilm composition, which has consequences for ecosystem CO2 exchange.

openCC0Jan 2023View details →

Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.

openCC0Jan 2023View details →

Soil-Adjusted Vegetation Index (SAVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include SAVI data with NDVI data presented in a companion dataset that is also available through the EDI.

openCC0Nov 2024View details →

Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023

Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>

openCC (other)Apr 2021View details →

Leaf area index for Spartina alterniflora near the GCE-LTER Flux Tower in 2018 and 2019

Leaf area index (LAI) was measured at permanent vegetation plots located in the GCE-LTER Flux Tower site for short and medium form Spartina alterniflora. LAI data were collected using a handheld ceptometer in 2018 and 2019.

openCC (other)Apr 2021View details →

Leaf area index and above ground biomass for Juncus roemerianus in the Grand Bay National Estuarine Research Reserve from 2015 to 2019

Leaf area index (LAI) and above ground biomass were measured in 16 permanent Juncus roemerianus vegetation plots located in the Grand Bay National Estuarine Research Reserve in Mississippi. Data were collected from 2015 to 2019. LAI were measured using a handheld ceptometer. Above ground biomass for each plot were collected from core samples.

openCC (other)Sep 2023View details →

Leaf Area Index on the GLBRC Biofuel Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009 to 2017)

Dataset AbstractThe leaf area index was measured to estimate the phenology and growth patterns of the different biofuel crops.original data source http://lter.kbs.msu.edu/datasets/225

openCustomApr 2025View details →

Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)

This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).

opencc-by-4.0May 2020View details →

Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data

This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.     1. Initial data used We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has  a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region  (45°S – 10°S / 25°W – 80°W).     2. The calculation method The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.     3. Data sets The data set contains two files:   - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)   - the yearly average of the EI (period 01/2003 - 12/2018) Two images are joined with this data set:   -  a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)      (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).   -  a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).      (file: Figure-enrichment-index.pdf) An improved version of this index will be available in a near future.

opencc-by-4.0Jul 2020View details →

Snow Index

Snow indexes are indicators of the snow present in the alps mountains. The indexes are calculated with an image processing pipeline. The dataset is a CSV file providing: snow index, snow percentage, latitude, longitude and date.   THIS WORK IS SHARED UNDER THE FOLLOWING LICENSE CREATIVE COMMONS ATTRIBUTION-SHAREALIKE 4.0 INTERNATIONAL (CC BY-SA 4.0) <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>

opencc-by-4.0May 2020View details →

CETAF-DiSSCo/COVID19-TAF biodiversity-related knowledge hub working group: indexed biotic interactions and review summary

This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/). This archive contains the biodiversity datasets of interest identified in period 14 April-6 October 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org).  GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software. These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group. If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email. Funding: The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 . References: 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. GloBI Data Review Report Datasets under review:  - Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org accessed via https://github.com/globalbioticinteractions/batbase/archive/9c65cfeee1a054f9db8cd8bf6892017fd1b3c840.zip on 2020-10-04T22:53:45.576Z  - Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/a2e1b57052244d5251d17e96ea61f58bea88975e.zip on 2020-10-04T22:54:28.727Z  - Daniel Becker, Gregory F Albery, Anna R Sjodin, Timothee Poisot, Tad Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A Han, Nancy B Simmons, Colin J Carlson. 2020. Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization. bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344 accessed via https://github.com/globalbioticinteractions/becker2020/archive/47c6ad28e1c5058f3c13ca69a59fdf21229e8d7f.zip on 2020-10-04T22:54:46.723Z  - Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/a906d76e362484d3ca1edbe9683f672838ab70b0.zip on 2020-10-04T22:56:13.913Z  - Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. accessed via https://github.com/globalbioticinteractions/drodvir/archive/0346c0e8d4d66c6400e9965bd6a6aeed24cd7586.zip on 2020-10-04T23:06:04.368Z  - Agosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . accessed via https://github.com/globalbioticinteractions/linnaeus1758/archive/a818060080fa04a88dac6df1ae5b897304ae8877.zip on 2020-10-05T00:46:04.852Z  - Mollentze, Nardus, & Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 accessed via https://github.com/globalbioticinteractions/mollentze2019/archive/ad12dc74d03c3d992618f16c37cafb7f7ffd9d01.zip on 2020-10-05T00:50:55.878Z  - Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip on 2020-10-05T00:53:53.646Z  - Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-10-05T00:56:43.356Z  - Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/ee8831a2a391203f4fa8c05a0ddd927202b234bf.zip on 2020-10-05T00:56:51.868Z  - Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-10-05T00:58:01.912Z  - De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-10-05T00:58:03.647Z  - Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-10-05T00:58:08.025Z  - Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463 accessed via https://github.com/globalbioticinteractions/shaw2020/archive/bb9ab857b7fdbb4e931752d01b43d37b3ada77cf.zip on 2020-10-05T01:05:23.554Z  - OpenBiodiv. 2020. Annotated biotic interaction tables from Pensoft publications. accessed via https://github.com/pensoft/pensoft-interaction-tables/archive/bb7d1dc9f2eba220a61502e06e6114053fd30788.zip on 2020-10-05T03:03:23.372Z  - Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://github.com/qgroom/batinterations/archive/70108945f9014aa0ac1db920191867f7e151c793.zip on 2020-10-05T03:04:11.533Z Generated on: 2020-10-06 by: GloBI's Elton 0.10.2 (see https://github.com/globalbioticinteractions/elton).   Note that all files ending with .tsv are files formatted as UTF8 encoded tab-separated values files. https://www.iana.org/assignments/media-types/text/tab-separated-values Included in this review archive are: README:   This file. review_summary.tsv:   Summary across all reviewed collections of total number of distinct review comments. review_summary_by_collection.tsv:   Summary by reviewed collection of total number of distinct review comments. indexed_interactions_by_collection.tsv:   Summary of number of indexed interaction records by institutionCode and collectionCode. review_comments.tsv.gz:   All review comments by collection. indexed_interactions_full.tsv.gz:   All indexed interactions for all reviewed collections. indexed_interactions_simple.tsv.gz:   All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName. datasets_under_review.tsv:   Details on the datasets under review. elton.jar:   Program used to update datasets and generate the review reports and associated indexed interactions. datasets.zip:   source datasets collected by elton in process of executing the generate_report.sh script. generate_report.sh:   program used to generate the report generate_report.log:   log file generated as part of running the generate_report.sh script

opencc-by-4.0Nov 2019View details →

Using the Tea Bag Index to unravel how interactions between an antibiotic (Trimethoprim) and endocrine disruptor (17a-estradiol) affect aquatic microbial activity: Supporting Dataset 1

The constant release of complex mixture of pharmaceuticals, including antimicrobials and endocrine disruptors, into the aquatic environment. These have the potential to affect aquatic microbial metabolism and alter biogeochemical cycling of carbon and nutrients. We used the Tea Bag Index (TBI) for decomposition within a series of contaminant exposure experiments to test how interactions between an antibiotic (trimethoprim) and endocrine disruptor (17a-estradiol) affects microbial activity in an aquatic system. The TBI is a citizen science tool used to test microbial activity by measuring the differential degradation of green and rooibos tea as proxies for labile and recalcitrant organic matter decomposition. Here we present the raw data on pharmaceutical exposures and the mass loss of the Rooibos and Green tea bags within the experiment. From Tea Bag mass loss we then calculated the Stabilisation Factor (S) and Initial Decomposition Rate of the labile organic matter fraction.

opencc-zeroMay 2023View details →

Documents first indexed in the SLUB catalog in 2022

The data set is available as a gzip-compressed, line-delimited JSON file and contains the 7,127,497 documents first indexed in the <a href="https://katalog.slub-dresden.de">SLUB catalog</a> in 2022 with the fields id (document identifier) and first_indexed (timestamp). The id consists of a creator id (optional), source id and a record id according to the scheme [{creator_id}-]{source_id}-{record_id}. If the record id contains characters that are unsuitable for URLs, it is base64-encoded without any padding. The first_indexed field, which is part of VuFind's Solr index schema, was determined using a Django application that regularly monitors the Solr cores of the SLUB catalog. The respective document sets from different points in time are compared with each other in order to determine new documents, i.e. document identifiers. If a new document is found, first_indexed is assigned the timestamp from the last_indexed field. The field obtained in this way serves as the basis for creating a list of new titles. However, it should be noted that neither all first indexed documents necessarily represent new titles, nor do the documents contained in this data set still have to be in the catalog. To check whether a document is currently in the SLUB catalog, its detailed view can be retrieved using the following URL scheme: https://katalog.slub-dresden.de/id/{id}. Example: <a href="https://katalog.slub-dresden.de/id/0-173837243X">https://katalog.slub-dresden.de/id/0-173837243X</a>.

opencc-zeroApr 2024View details →

Documents first indexed in the SLUB catalog in 2023

The data set is available as a gzip-compressed, line-delimited JSON file and contains the 17,182,153 documents first indexed in the <a href="https://katalog.slub-dresden.de/">SLUB catalog</a> in 2023 with the fields id (document identifier) and first_indexed (timestamp). The id consists of a creator id (optional), source id and a record id according to the scheme [{creator_id}-]{source_id}-{record_id}. If the record id contains characters that are unsuitable for URLs, it is base64-encoded without any padding. The first_indexed field, which is part of VuFind's Solr index schema, was determined using a Django application that regularly monitors the Solr cores of the SLUB catalog. The respective document sets from different points in time are compared with each other in order to determine new documents, i.e. document identifiers. If a new document is found, first_indexed is assigned the timestamp from the last_indexed field. The field obtained in this way serves as the basis for creating a list of new titles. However, it should be noted that neither all first indexed documents necessarily represent new titles, nor do the documents contained in this data set still have to be in the catalog. To check whether a document is currently in the SLUB catalog, its detailed view can be retrieved using the following URL scheme: https://katalog.slub-dresden.de/id/{id}. Example: <a href="https://katalog.slub-dresden.de/id/0-185178604X">https://katalog.slub-dresden.de/id/0-185178604X</a>.