cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21559080.v1
title: Tarp impacts on soil arthropods

description: These data show the impacts of tarps on aboveground and belowground arthropods (collected with pitfall traps and the Berlese funnel method, respectively). These data are a timeseries with 5 sampling times (before tarping, during tarping, and 1, 3, and 5 weeks after tarp removal). <br> We identified arthropods into morphospecies and calculates species richness, Shannon’s diversity, and total abundance. We also include the abundance of certain pest and natural enemy species. <br> For more information, please refer to Kinnebrew et al. 2022 (https://doi.org/10.1080/21683565.2022.2146254) or contact Dr. Kinnebrew at eva.kinnebrew@colostate.edu. <br> Column Names and Explanations: <br> 1. Farm: the farm site (“A,” “B” or “C”) where the plot was located. All farms were located in Chittenden County, Vermont, USA <br> 2. Treatment: indicates the treatment type (silage tarps, clear plastic tarps, or control) <br> 3. Plot_ID: the unique identifier for each spatial plot <br> 4. Sampling: the sampling period. “Pretarps” indicates sampling that occurred a week before tarp application, to capture baseline understanding of the arthropod community. “Tarps on” indicates a sampling that took place 3 weeks into tarp application. “Tarps off 1w,” “Tarps off 3w,” and “Tarps off 5w” indicates samplings that took place 1, 3, and 5 weeks after tarps were removed from the plots. <br> 5. Pitfall_SpRichness: species richness (specifically the number of morphospecies) of arthropods collected with pitfall traps <br> 6. Pitfall_Shannons: Shannon’s diversity of arthropod morphospecies collected with pitfall traps <br> 7. Pitfall_TotalOrgs: total number of individuals collected with pitfall traps <br> 8. Berlese_SpRichness: species richness (specifically the number of morphospecies) of arthropods collected with the Berlese funnel method <br> 9. Berlese_Shannons: Shannon’s diversity of arthropod morphospecies collected with the Berlese funnel method <br> 10. Berlese_TotalOrgs: total number of individuals collected with the Berlese funnel method <br> 11. Chrysomelidae_Abundance: abundance (number of individuals) per plot in the Chrysomelidae family (flea beetles). Organisms captured with pitfall traps. <br> 12. Aphididae_Abundance: abundance (number of individuals) per plot in the Aphididae family (aphids). Organisms captured with pitfall traps. <br> 13. Carabidae_Abundance: abundance (number of individuals) per plot in the Carabidae family (ground beetles). Organisms captured with pitfall traps. <br> 14. Elateridae_Abundance: abundance (number of individuals) per plot in the Elateridae family (click beetles). Organisms captured with pitfall traps. <br> 15. Lycosidae_Abundance: abundance (number of individuals) per plot in the Lycosidae family (wolf spiders). Organisms captured with pitfall traps. <br> 16. Curculionidae_Abundance: abundance (number of individuals) per plot in the Curculionidae family (weevils). Organisms captured with pitfall traps.
DOI: 10.5281/zenodo.7278141
title: Notary network (Notarissennetwerk)

description: RDF version of the Notary Network (Notarissennetwerk, https://notarissennetwerk.nl/), a structured version of the Repertorium van Notarissen. The dataset is included in the Golden Agents project (https://www.goldenagents.org/). The repo was forked from https://gitlab.com/uvacreate/vondel/notarissennetwerk-rdf/ and has been updated to fit in the Golden Agents infrastructure: All schema.org resources now use https The prefix is changed from https://data.create.humanities.uva.nl/id/ to https://data.goldenagents.org/datasets/
description: If you use this dataset, please cite it using the metadata from this file.
DOI: 10.5281/zenodo.7278142
title: Notary network (Notarissennetwerk)

description: RDF version of the Notary Network (Notarissennetwerk, https://notarissennetwerk.nl/), a structured version of the Repertorium van Notarissen. The dataset is included in the Golden Agents project (https://www.goldenagents.org/). The repo was forked from https://gitlab.com/uvacreate/vondel/notarissennetwerk-rdf/ and has been updated to fit in the Golden Agents infrastructure: All schema.org resources now use https The prefix is changed from https://data.create.humanities.uva.nl/id/ to https://data.goldenagents.org/datasets/
description: If you use this dataset, please cite it using the metadata from this file.
DOI: 10.6073/pasta/5bdbbc8c2c9ae67ef5dfbc4e00ed7ac7
title: North Temperate Lakes LTER: Summary of Microbial Activity 2000 - 2002

description: Summary of Microbial Observatory data from the bacterial production, planktonic respiration and alakline phosphatase activity databases, plus bacterial cell counts from epifluorescence microscopy using DAPI cell stain. Information on integrated sample depth and incubation temperature is also included Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 4
DOI: 10.6084/m9.figshare.21559092
title: Tarp impacts on soil temperature

description: These data show the impacts of tarps on soil temperature, and we present the raw continuous temperature measurements for 1 month during tarp application. We buried iButtons under 1 cm and 10 cm of soil in each plot to measure soil surface and soil temperature, respectively. Temperatures were automatically taken every 30 minutes, and specific time and dates of each soil or soil surface temperature measurement are given in the “Date_soil” and “Date_surface” columns, respectively. <br> For more information, please refer to Kinnebrew et al. 2022 (https://doi.org/10.1080/21683565.2022.2146254) or contact Dr. Kinnebrew at eva.kinnebrew@colostate.edu. <br> Column Names and Explanations: <br> 1. Farm: the farm site (“A,” “B” or “C”) where the plot was located. All farms were located in Chittenden County, Vermont, USA <br> 2. Treatment: indicates the treatment type (silage tarps, clear plastic tarps, or control) <br> 3. Plot_Replicate: the treatment replicate number on each farm (1-6 for each treatment on each farm) <br> 4. Plot_ID: the unique identifier for each spatial plot <br> 5. Temp_soil: temperature in Celsius measured 10 cm below the soil surface <br> 6. Temp_surface: temperature in Celsius measured at the soil surface (1 cm below the surface) <br> 7. Date_soil: date associated with the measurement of soil temperature <br> 8. Date_surface: date associated with the measurement of the soil surface temperature
DOI: 10.34681/4zf4-4w34
title: Quality Improvement Center for Adoption and Guardianship Support and Preservation

description: The Children’s Bureau, Administration for Children and Families, Department of Health and Human Service established the National Quality Improvement Center for Adoption and Guardianship Support and Preservation (QIC-AG). In October 2014, the QIC-AG was awarded to Spaulding for Children in partnership with The University of Texas at Austin, The University of Wisconsin at Milwaukee, and The University of North Carolina at Chapel Hill. The QIC-AG was designed to promote permanence, when reunification is no longer a goal, and improve adoption and guardianship preservation and support.

For five years, the QIC-AG team worked with eight sites across the nation, with the purpose to implement evidence-based interventions or develop and test promising practices which, if proven effective, could be replicated or adapted in other child welfare jurisdictions. However, for the NDACAN archive, data from only 6 of the sites are included. These sites are from the following jurisdictions: Catawba County (NC), Illinois, New Jersey, Tennessee, Vermont and Wisconsin.

The six sites included in the NDACAN archive all served the following broad TARGET POPULATION, defined by the funder as: “Children and youth and their adoptive or guardianship families who have already finalized the adoption or guardianship and for whom stabilization may be threatened will also be targeted for support and service interventions. The children and youth in this target group may have been adopted through the child welfare system or by private domestic or intercountry private agency involvement.”

The primary RESEARCH QUESTION was: Do families with a finalized adoption or guardianship have increased post-permanency stability and improved well-being if they receive post permanency services and support compared with similar families who receive services as usual?

The THEORY OF CHANGE suggests that predictors of post-permanency instability can include: (1) caregivers’ assessment of child or youth behavior problems and (2) caregivers’ self-report of their caregiving commitment (Testa, et al, 2015). Site-specific interventions should target families most at risk of post-permanency instability. Post-permanency stability can be maintained by checking-in with families after finalization to identify needs and assess permanency commitment. By providing post-permanency services and support, the capacity of caregivers to address the needs of the children in their care will increase and reduce the needs of these children. Families who are provided with services and support will have increased capacity for post-permanence stability and improved well-being.

The project’s short-term outcomes varied by site and included, for example, increased level of caregiver commitment; reduced levels of family stress; improved familial relationships; and reduced child behavioral issues. The project had three long-term outcomes: increased post-permanency stability, improved behavioral health for children, and improved child and family well-being.
DOI: 10.5281/zenodo.6883677
title: Golden Agents - Occasional Poetry (case study)

description: Scripts and data accompanying the Semantics2022 paper (TBA).
DOI: 10.5281/zenodo.6883678
title: Golden Agents - Occasional Poetry (case study)

description: Scripts and data accompanying the Semantics2022 paper (TBA).
DOI: 10.6084/m9.figshare.21559095
title: Tarp impacts on soil nitrate and soil moisture

description: These data demonstrate the impact of tarps on soil nitrate availability and soil moisture. We measured soil nitrate availability 2-3 days before and after tarp application and include the difference between these two time periods to show how tarps change soil nitrate availability. We additionally include here soil moisture measurements 3 weeks into the tarp application. <br> For more information, please refer to Kinnebrew et al. 2022 (https://doi.org/10.1080/21683565.2022.2146254) or contact Dr. Kinnebrew at eva.kinnebrew@colostate.edu. <br> Column Names and Explanations: <br> 1. Farm: the farm site (“A,” “B” or “C”) where the plot was located. All farms were located in Chittenden County, Vermont, USA <br> 2. Treatment: indicates the treatment type (silage tarps, clear plastic tarps, or control) <br> 3. Plot_Replicate: the treatment replicate number on each farm (1-6 for each treatment on each farm) <br> 4. Plot_ID: the unique identifier for each spatial plot <br> 5. Nitrate_PreTarping: nitrate availability (soil nitrate-N mg/kg) measured 2-3 days before tarp application (20 May 2021) <br> 6. Nitrate_PostTarping: nitrate availability (soil nitrate-N mg/kg) measured 2-3 days after tarp removal (23 June 2021) <br> 7. Nitrate_Change: Change in nitrate availability with tarping (soil nitrate-N mg/kg), calculated as post tarping minus pre tarping nitrate values <br> 8. Soil_Moisture: volumetric water content (%) of soil, measured with TDR soil probes on 16 June 2021
DOI: 10.6084/m9.figshare.21559149
title: Metadata for tarping datasets (Kinnebrew et al. 2022)

description: This document contains metadata associated with the datasets published in the FigShare collection: "Data associated with: Tradeoffs of a rising agroecological practice: addressing uncertainty around tarping with participatory action research and mixed methods" <br> For more information, please refer to Kinnebrew et al. 2022 (https://doi.org/10.1080/21683565.2022.2146254) or contact Dr. Kinnebrew at eva.kinnebrew@colostate.edu.
DOI: 10.6084/m9.figshare.21559149.v1
title: Metadata for tarping datasets (Kinnebrew et al. 2022)

description: This document contains metadata associated with the datasets published in the FigShare collection: "Data associated with: Tradeoffs of a rising agroecological practice: addressing uncertainty around tarping with participatory action research and mixed methods" <br> For more information, please refer to Kinnebrew et al. 2022 (https://doi.org/10.1080/21683565.2022.2146254) or contact Dr. Kinnebrew at eva.kinnebrew@colostate.edu.
DOI: 10.5281/zenodo.7324273
title: Human Respiration Dataset - Training purposes

description: This is a training dataset holding the heart rate (hr), blood oxygenation (ox), pulmonary pressure (pp), and the control variable (ctrl) or pushed per minute that a mechanical ventilator should perform under the given conditions. We include the results on a healthy patient with the respirator configured to update the working frequency every 30 seconds (Capturing 5 minutes total).
DOI: 10.6073/pasta/7d82e9bf6b788e21d7b54b8257e06319
title: Microbial Bacterial Production in Lakes at North Temperate Lakes LTER 2000 - 2002

description: Net production of bacteria passing a 70 micron mesh, or bacteria passing a 1 micron mesh, calculated incorporation of 3H labeled leucine into cell proteins. Method based on microcentrifuge method of Smith, David C., and Farooq Azam. 1992. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine, Marine Microbial Food Webs 6(2):107-114. Nanomolar treatments refer to leucine concentration for calculation of isotopic dilution and necessary leucine concentration to saturate uptake into bacterial cells. For a good example on how to calculate isotopic dilution see Pace. Michael L., and Jonathan J. Cole. Primary and bacterial production in lakes: are they coupled over depth? Plankton Research 16(6):661-672. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 4
DOI: 10.5519/qd.eb9ypzpl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.20291523
title: Seedling survival data from one-year precipitation manipulation in northern Vermont, USA

description: Data include seedling survival outcomes from a field-based precipitation manipulation experiment. <br> For complete methods and experimental design, see: <br> Clark, P. W., & D’Amato, A. W. (2022). Seedbed not rescue effect buffer the role of extreme precipitation on temperate forest regeneration. Ecology.
DOI: 10.6084/m9.figshare.20291523.v1
title: Seedling survival data from one-year precipitation manipulation in northern Vermont, USA

description: Data include seedling survival outcomes from a field-based precipitation manipulation experiment. <br> For complete methods and experimental design, see: <br> Clark, P. W., & D’Amato, A. W. (2022). Seedbed not rescue effect buffer the role of extreme precipitation on temperate forest regeneration. Ecology.
DOI: 10.5281/zenodo.6382057
title: Various PubChem lists for Parkinson's Disease and related disorders

description: Collection of 8 lists of chemicals associated to Parkinson's disease (PD) and related disorders. PubChem functionality relating Medical Subject Headings (MeSH) information on certain disease endpoints to chemicals was explored to create the lists. The following table describes the mentioned lists. Name Description PD-MetFrag MetFrag database of 21,303 chemicals related to PD, PD Secondary, Parkinsonian disorders, Lewy body disease, Alzheimer disease, dementia, Huntington disease and neurodegenerative disorders PD-CTD List of 296 chemicals associated to PD in the Comparative Toxicogenomic Database (CTD), extracted via the CTD integration within PubChem D003248 List of 3,943 chemicals related to Constipation Disorders D000857 List of 688 chemicals related to Olfactory Impairment D003863 List of 363 chemicals associated to Depression Disorders D001008 List of 8,433 chemicals associated to Anxiety Disorders D020920 List of 59 chemicals related to Dyssomnias D012893 List of 2,781 chemicals associated to Sleep Wake Disorders Further details can be found in the published manuscript (https://doi.org/10.1007/s00216-022-04207-z).
DOI: 10.5281/zenodo.6382056
title: Various PubChem lists for Parkinson's Disease and related disorders

description: Collection of 8 lists of chemicals associated to Parkinson's disease (PD) and related disorders. PubChem functionality relating Medical Subject Headings (MeSH) information on certain disease endpoints to chemicals was explored to create the lists. The following table describes the mentioned lists. Name Description PD-MetFrag MetFrag database of 21,303 chemicals related to PD, PD Secondary, Parkinsonian disorders, Lewy body disease, Alzheimer disease, dementia, Huntington disease and neurodegenerative disorders PD-CTD List of 296 chemicals associated to PD in the Comparative Toxicogenomic Database (CTD), extracted via the CTD integration within PubChem D003248 List of 3,943 chemicals related to Constipation Disorders D000857 List of 688 chemicals related to Olfactory Impairment D003863 List of 363 chemicals associated to Depression Disorders D001008 List of 8,433 chemicals associated to Anxiety Disorders D020920 List of 59 chemicals related to Dyssomnias D012893 List of 2,781 chemicals associated to Sleep Wake Disorders Further details can be found in the published manuscript (https://doi.org/10.1007/s00216-022-04207-z).
DOI: 10.5281/zenodo.7324261
title: Datasets - Vermilyea et al 2022

description: Datasets for Vermilyea et al., 2022: "Loss of tau expression attenuates Neurodegeneration associated with a-synucleinopathy"
DOI: 10.5281/zenodo.7324262
title: Datasets - Vermilyea et al 2022

description: Datasets for Vermilyea et al., 2022: "Loss of tau expression attenuates Neurodegeneration associated with a-synucleinopathy"
DOI: 10.25625/dwc16s/7auxhn
title: functionalGroups.tab

description: :unav
DOI: 10.25625/dwc16s
title: Pollen insect connections in enrichment plot

description: Data from understory vegetation surveys of floral resources and insects, conducted in the B11 biodiversity enrichment experiment plots in November/December 2017. In three rounds, a pair of surveyors each walked a random 10 m transect within each plot, identifying and counting all flower units (single flowers or inflorescences) within one meter of either side of the transect. Transects were allowed to change direction to conform to space limitations of the plot, e.g., to stay within small plots or when transects were near plot borders. Within the understory vegetation of each plot, a surveyor made five evenly spaced sweeps, covering ca. 1 m of vegetation each, along a randomly placed 5 m transect using a 32 cm diameter sweep net (BioForm, Nuremburg, Germany). All flying insects (i.e., excluding Formicidae) were collected and individually preserved on site in 1.5 ml Eppendorf tubes with 70% ethanol. Insects were cleansed of pollen and identified. We identified samples to family using a binocular microscope and general and region-specific guides (Goulet et al. 1993, Johnson and Triplehorn 2004, Bosuang et al. 2017). Pollen removed from insects was acetolyzed and concentrated. The resulting pollen solution was centrifuged to further concentrate the pollen and the solution at the bottom was made into a microscope slide and scanned visually for pollen and identified.
DOI: 10.25625/dwc16s/1wvuft
title: PollenInsectsNetwork.tab

description: :unav
DOI: 10.25625/dwc16s/dgkop0
title: insects_pollen.tab

description: :unav
DOI: 10.25625/dwc16s/gsyhvk
title: pollen.tab

description: :unav
DOI: 10.25625/dwc16s/nl8rda
title: insects.tab

description: :unav
DOI: 10.25625/ezcik3/w24rpf
title: visits.tab

description: Data collected in 5-min insect surveys. See readme document in metadata.
DOI: 10.25625/ezcik3/6mba48
title: harvest.tab

description: Plant-level fruit yield data. See readme document in metadata.
DOI: 10.25625/ezcik3/8qmei8
title: plants.tab

description: Plant status data during monitoring and harvest periods. See readme document in metadata.
DOI: 10.25625/ezcik3/b6dos3
title: Phytometer_dataset.tab

description: Combined Excel version of csv files.
DOI: 10.25625/ezcik3/xzjlly
title: plotinfo.tab

description: Plot-level info. See readme document in metadata.
DOI: 10.25625/ezcik3/z6zdfq
title: fruit.tab

description: Fruit-level yield data taken from 10-fruit subset. See readme document in metadata.
DOI: 10.25625/ezcik3
title: Enrichment plots phytometer plants

description: Phytometer plants were used to assess the ecosystem service provisioning within the B11 enrichment plots. Chili pepper plants (Capsicum annuum) were used for this purpose, as its fruit production is partially dependent on pollination. Chili plants were raised from seed starting October 2017. Before transfer to field sites, NPK fertilizer and pesticide (imidacloprid, deltamethrin, mancozeb, and abamectin) were applied following local practices to standardize growing conditions and control pest damage. We halted fertilizer and pesticide application one week prior to placement in the plots and only watered as conditions required thereafter. Immediately prior to placement, we removed any flowers that had already opened to ensure pollination could be attributed to plot conditions. Four chili plants were placed in each enrichment plot, approximately in the center of the plot, avoiding subplot and study areas of other experiments. We placed the four chili plants in each plot together, with plant stems forming a 60 cm square at approximately the plot center in a location receiving at least partial sunlight and free from encroaching vegetation. Plants were kept in their original polyethylene containers to isolate them from local soil conditions. For 20 of the enrichment plots, 4 chili plants were also placed 15m outside of the plot, along the same cardinal direction of the monitored palm trees. The phytometer chili plants remained in the enrichment plots for five weeks, during which time assistants conducted five rounds of pollinator monitoring and plant assessments. Pollinator monitoring consisted of visual observation for 5 minutes and recording the taxonomic order of insects that visited flowers and their visiting time. Plant assessments included biometrics such as plant height, number of leaves, number of plants, and number and extent of pest damage. At the same time that chili plants were placed in the plots, two other treatments were conducted in a nursery area near the Humusindo field station. One treatment was the complete exclosure of pollinators by placing netting around plants. Another treatment was complete pollination of all flowers using manual pollination. Treatments groups had 11 and 12 chili plants, respectively. Once phytometer plants began to fruit, 10 developing fruits were marked for tracking fruit development success. Once fruits matured, they were harvested, counted and weighed. A subset of 10 fruits from each plant were subsequently dried and their seeds were counted.
DOI: 10.25625/ezcik3/een53t
title: readme.txt

description: Explanation of columns in csv files.
DOI: 10.25625/ezcik3/fxmlet
title: Phytometer Protocol_long.pdf

description: Detailed experimental setup and data collection protocol
DOI: 10.25625/brb1bc/pkerdt
title: B11_FloralSurvey.tab

description: Main excel file, contains readme tab