Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “Pledge”
Data for study "Realisation of Paris Agreement pledges may limit warming just below 2°C"
<p>This is the data repository with country-level data and individual scenario data for the study “Realisation of Paris Agreement pledges may limit warming just below 2°C”. Please find below a description of the different data sources available. </p> <p><strong>Emissions Scenario Data (scenarios_12Nov2021a_CR.csv). </strong>The complete set of infilled and extended emissions for each scenario presented in the study. The emissions timeseries span from 2015 to 2100 for 52 emissions species required to run MAGICC7.</p> <p><strong>Scenario Statistics Table (stats_12Nov2021a_CR_short.csv).</strong> Summary statistics for the scenarios used in the sensitivity study. Statistics available include Exceedance probabilities, peak warming, year of peak warming and warming in 2100.</p> <p><strong>Supplementary Table S1 (supplementary_table-s1.csv) - </strong>Estimate of total GHG emissions (excl. LULUCF) in 1990, 2010, 2019 and for 2025 and 2030 under the NDCs. The ‘lower’ and ‘higher’ end of the range span the four cases of full implementation and ‘unconditional-only’ NDC quantification, with their respective ranges (e.g. Australia’s 26%-28% target). Historical data based on country-level data in NDCs and PRIMAP-hist CR. Aggregation of CO2, CH4, N2O, HFCs, PFCs, and SF6 emissions performed in GWP-100 AR6 metrics. Note that the common reporting guidelines under the Paris Agreement’s transparency framework currently chose GWP-100 AR5 metric as default (Decision 5/CMA.3, UNFCCC, 2021).</p> <p><strong>Supplementary Table S2 (supplementary_table-s2.csv) </strong>An estimate of potentially additional methane reductions if the Global Methane Pledge were implemented on a country-by-country level with a 30% reduction. Of the 104 countries signed up by 2 Nov 2018, 101 are here considered (with no sufficient methane data availability for Niue, Palau and Federal State of Micronesia).<strong> </strong></p> <p><strong>Scenario Timeseries (timeseries_12Nov2021a_CR.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for NDC scenarios presented in the study.<strong> </strong></p> <p><strong>IEA Scenario Timeseries (timeseries_IEA.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for the IEA scenarios presented in the study.</p> <p>Contact Jared Lewis <jared.lewis@climate-resource.com> for additional data about the target scenarios.</p>
Ecosystem restoration pledges in a telecoupled and unequal world v1.0.1
<p>This contains the script and data necessary to replicate the figures created in the article:<strong> </strong>"<span>National ecosystem restoration pledges are mismatched with social-ecological enabling conditions</span>"<strong> </strong>in the journal Communications Earth and Environment.</p> <p> </p> <p><em>Felipe Benra<sup>1</sup>*, Maria Brück<sup>1</sup>, Emily Sigman<sup>1</sup>, Manuel Pacheco-Romero<sup>1,</sup></em><sup>2</sup><em>, Girma Shumi<sup>3</sup>, David J Abson<sup>1</sup>, Marina Frietsch<sup>1</sup>, Joern Fischer<sup>1</sup></em></p> <p><sup>1</sup>Social-Ecological Systems Institute, School of Sustainability, Leuphana University Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany</p> <p><sup>2</sup>Centro Andaluz para el Cambio Global - Hermelindo Castro (ENGLOBA), Universidad de Almería, Carretera de Sacramento, s/n 04120 La Cañada de San Urbano, Almería, Spain</p> <p><sup>3</sup>Independent researcher, Graf-von-Moltke Str. 2, 21337 Lüneburg, Germany</p> <p>*corresponding author</p>
Honesty Pledges to reduce Unethical Behavior
<p>Authorities and managers often rely on individuals and businesses' self-reports and employ various forms of honesty declarations to ensure that those individuals and businesses do not over-claim payments, benefits, or other resources. While previous work has found that honesty pledges have the potential to decrease dishonesty, effects have been mixed. We argue that understanding and predicting when honesty pledges are effective has been obstructed due to variations in experimental designs and operationalizations of honesty pledges in previous research. Specifically, we focus on the role of whether and how an ex-ante honesty pledge asks individuals to identify (by ID, name, initials) and how much involvement the pledge requires from the individual (low: just reading vs. high: re-typing the text of the pledge). In four pre-registered online studies (<em>N</em> > 5000), we systematically examine these two dimensions of a pledge to find that involvement is often more effective than identification. In addition, low involvement pledges, without any identification, are mostly ineffective. Finally, we find that the effect of a high (vs. low) involvement pledge is relatively more persistent across tasks. Yet, repeating a low involvement pledge across tasks increases its effectiveness and compensates for the lower persistency across tasks. Taken together, these results contribute both to theory by comparing some of the mechanisms possibly underlying honesty pledges as well as to practice by providing guidance to managers and policymakers on how to effectively design pledges to prevent or reduce dishonesty in self-reports.</p>
Figure 5. Ektopodon stirtoni Pledge, 1986 in New specimens of ektopodontids (Marsupialia: Ektopodontidae) from South Australia
Figure 5. Ektopodon stirtoni Pledge, 1986, holotype dentary and new material. a-c. new maxilla (SAM P35309): a. lateral; b. dorsal; c. palatal; d. RM2 (P23854); e. LM3 (P30175); f. LM3 (SAM P30156); g. right dentary (SAM P29577), lateral aspect; h. right dentary with M (SAM 2-4 P29577) stereo pair; i, j. holotype right dentary with P, M (SAM P19509);k. LM (P31638); l. RM4 (SAM P33451). Ngama Quarry, Mammalon 3 1-3 3 Hill, Lake Palankarinna; Ngama Local Fauna.
Plan and Pledge, HIV Self-testing in South Africa
ClinicalTrials.gov study NCT03898557. IPD Sharing: NO. Countries: 1. Publications: 4.
Model and input files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )
<p>The GCAM model (GCAMv5.3 NDC) and input files used to conduct Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )</p> <ol> <li>The model needs to be compiled using third-party libraries (see https://jgcri.github.io/gcam-doc/gcam-build.html). Source code has been included in the GCAMv5.3_NDC/csv</li> <li>Default GCAM input files have been included in GCAMv5.3_NDC/input/gcamdata/xml</li> <li>Additional input files for NDC scenarios have been included in GCAMv5.3_NDC/input/NDC_Ratchet_policy</li> <li>A sample configuration_NDC_sample.xml has been included in GCAMv5.3_NDC/exe with detailed setup instruction</li> </ol>
GCAM output files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )
<p>Original GCAM output files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming)</p> <p>A "Naming Rule" file is included. More details and assumptions can be found in the original paper. GCAM documentation can be found at https://jgcri.github.io/gcam-doc/ </p>
Source code and data for Ou et al. (2021) Updates to Paris climate pledges improve chances of limiting global warming to well below 2°C
<p>There are two folders in this repository. The <strong>GCAM-model</strong> folder contains the version of GCAM5.3 used to estimate emission pathways for this analysis. The <strong>data</strong> folder contains source data for our main results. Please check readme.pdf and our original paper for details. </p> <p> </p> <p> </p>
Data for Study "Towards a transparent framework for assessing the revisions of national climate pledges after the Global Stocktake"
<p>This is the data repository with global and country-level data for individual scenarios used in the study “<strong>Towards a transparent framework for assessing the revisions of national climate pledges after the Global Stocktake</strong>”.</p>
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.